Fault prediction system for electrical distribution systems and monitored loads

ABSTRACT

A system for fault prediction in electrical systems. It includes a network of recording nodes that transmit data to multi-channel recorder. The nodes monitor power quality based on a number of system parameters. These data are assembled into data blocks and are analyzed to determine load factors for the system over time. Data blocks are collected over 22 cycle periods and are evaluated against a derived standard deviation factor for the given system. The standard deviation is used to determine alert and alarm levels. The constant monitoring allows the system to alert workers of a potential upcoming fault in one or more system components. In this way repairs can be made before the component fails and the system experiences a fault condition.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims benefit of Provisional application 61/680,807filed Aug. 8, 2012.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH AND DEVELOPMENT

Not Applicable

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates to systems of fault prediction and particularlyfault prediction systems for electrical distribution systems andmonitored loads.

2. Description of the Prior Art

Electrical power has become the most essential commodity of our everydayneeds. In manufacturing, where systems like “lean manufacturing” and“just in time” delivery philosophies are pervasive, a reliable anddependable electrical distribution system is more critical to abusiness's fiscal health than ever before. Unfortunately, globalcompetition has forced production quotas in many companies andindustries to increase, while at the same time, forcing many companiesto make reductions in their labor force and maintenance budgets as theyattempt to reduce operating costs. Such cost savings measures can beexpensive. Insurance claims data published in the National FirePrevention Association (NFPA) publication 70B maintenance standard,shows that almost half of the cost associated with electrical failurescould have been prevented. The typical service life of electricalequipment is generally 25-30 years depending on maintenance and upkeep,and much of the aging infrastructure has been in service for 20 years orlonger. With electrical distribution systems aging year-by-year, andpreventative maintenance programs being cut to improve the corporatebottom line, the likelihood of a facility experiencing a catastrophicfailure to its electrical distribution system is increasing.

Another concern for those responsible for the maintenance of electricalsystems is that of electrical transients. An electrical transient is atemporary excess of voltage and/or current in an electrical circuit thathas been disturbed. Transients are short duration events, typicallylasting from a few thousandths of a second (milliseconds) to billionthsof a second (nanoseconds), and they are found on all types ofelectrical, data, and communications circuits. The electricaldistribution system and attached load equipment is under constant attackfrom various types of power line disturbances. The result is anestimated $26 billion-per-year cost to U.S. companies in lost time,equipment repair, and equipment replacement. Transient voltage surgescomprise the most severe and immediate danger to sensitive electricaland electronic equipment, and are often a neglected aspect of facilitydesign.

Studies have shown that approximately 80% of transient activity at agiven facility may be internally generated. From the normal on-and-offswitching of copiers, heating and ventilation, capacitor banks, and airconditioning systems to robotic assembly and welding machines,practically every industrial machine or system causes or is adverselyaffected by transients. Surges and transient power anomalies arepotentially destructive electrical disturbances, the most damaging beingover-voltage occurrences and short duration over-voltage events, and thecumulative effect of these transients are a major source ofsemi-conductor degradation and failure. Thus, a system that can predictelectrical faults in enough time to correct them before they causeserious problems is needed.

BRIEF DESCRIPTION OF THE INVENTION

This invention overcomes the limitations of the systems discussed above.It does this by doing load analysis, which generally includes monitoringa load's electrical current by measuring and recording its value overtime and producing an alarm when some static threshold is exceeded. Thisrequires developing a means to identify the operational characteristicsthat define the load. The operational characteristics of an electricalload are fundamentally defined by how the load draws current from avoltage source. This means that load analysis is synonymous with currentanalysis. Current analysis identifies current as more than just aquantity or magnitude of amps flowing through a conductor. Current isalso identified by its displacement factor with respect to the voltage,referred to here as power factor, and by the Total Harmonic Distortion,or THD, of its sine wave. These three elements of the load current, itsRoot Mean Square (RMS) value, power factor and THD are used to createframes of reference for describing the operational characteristics of aload.

A fourth element, the standard deviation of the RMS current, is alsoused as a frame of reference. Once this framework is developed, theoperational boundaries can be identified, and thresholds based uponthese boundaries determined. The system is designed to automaticallyselect the threshold values for the analysis that generates the alerts,warnings and alarms for the system.

Once accurate thresholds are developed, the automating reliable systemresponse is a matter of creating predetermined patterns of response tothe various thresholds that may be crossed. It can be programmed toinclude active responses for the mitigation of developing faults andconditions such as automated power factor correction, where capacitorbanks are automatically brought on or off line in response to thechanging power factor measured at the main service.

Quantifying the volatility of an electrical system allows for predictiveanalysis.

The concept of volatility as a unique measure for the study ofelectrical variables is fundamental to this invention. Analyzingvolatility increases dramatically the ability for predicting electricalfaults. For example, a fault condition arises when the normal electricalvalues associated with the proper operation of an electrical system areexceeded. The first indication of such a change would be a change in therelative volatility of one or more of the electrical values that definethe operating parameters for that system. Measuring this volatility andtrending the volatility of these values over time reveals the earliestpossible warning that a change has occurred in the stability of thatsystem. Typical power quality monitoring uses predetermined staticvalues as thresholds to alert of a possible problem. The trouble withthis method is that waiting for a threshold to be crossed by someelectrical variable before being alerted that a problem exists is beinginformed that a problem exists when it already exists. While this may beinformative, it's not predictive.

Because the automated predictive analysis method is fundamentallycomparative, a baseline of values for comparison purposes must first beestablished for each load that is monitored before any meaningfulanalysis can be performed. Once a baseline of values has been created,threshold values are then determined that initiate the notificationprocess.

The load profile for a load is constructed by averaging the electricalvalues of that load over several load cycles. A load cycle is thatperiod of time starting with when the load is initially turned on andcontinues until it is turned off or disconnected. A load cycle isdefined as a collection of concurrently created data blocks, whose I-RMSvalues exceed the circuit's ambient noise levels. A load cycle beginswith one or more single data blocks whose Delta I-RMS value goespositive and whose value exceeds compression limits. The load cycle'sI-RMS value then arrives at some relatively stable point where datablock compression can begin. The load cycle continues, as a collectionof sequentially created compressed and single data blocks, depending onload stability, until ending with a single data block whose Delta I-RMSis negative, and whose I-RMS value returns to the circuit's ambientnoise level. The data block values used in defining the operationalcharacteristics for a load cycle, and consequently, the construction ofa load profile is taken from the current half of the data block.

Once a load cycle begins, the multi-channel recorder's database takesthe electrical values in the current half of the compressed data blocks,plus the current's THD and begins to calculate the load cycles baselinevalues. These values are used to define the load's operationalcharacteristics.

The fourth value used in defining the operational characteristics of aload is the I-RMS standard deviation value, measured for each compresseddata block contained in the load cycle. As explained earlier, this is ameasure of the average variance of the RMS current measured for thatdata block and quantifies its volatility. This value is averaged overtime and used as a gage to denote the relative stability or volatilityof the load in how it draws current during a load cycle. A suddenincrease in this value indicates an increase in the volatility of thecurrent and indicates a potential disequilibrium occurring within theload.

The power factor value, the current THD value and the I-RMS standarddeviation value to their corresponding RMS current value provides acommon frame of reference by which the relationship between these fourvariables can be quantified. Their average values, over the span of theload cycle, are used to determine the standard deviation for eachvariable, which is used in defining its corresponding operationalboundary and thereby determining threshold values.

The standard deviation, which defines the operational boundaries for thevariable in question, permits the system being monitored to defineitself rather than using some predetermined static value. This isimportant because each load must be considered as unique if accurateload profiles are to be developed that define the operational boundariesof the load. Using standard deviation also facilitates the ability toautomate this function because the operational boundary of a loaddefines the threshold values that are used to drive the automatedpredictive analysis method.

The many different possible combinations provided by the four loadprofile elements creates the potential for characterizing the unique waythat a load responds to conditions that ultimately drive it to alarmstatus. Collecting such historical records of a load could beexceedingly valuable in the further development of the capabilities ofautomated predictive analysis.

Thus, it is an object of the invention to provide automatic notificationof potential fault conditions prior to those faults occurring.

It is another object of the invention to provide continuous monitoringof power systems.

It is yet another object of the invention to fully automate predictiveanalysis methods and apply them to the preventative and predictivemaintenance system.

It is yet another object of the invention to produce automaticallygenerated work orders detailing the preventative actions to be taken torepair a piece of equipment before it fails.

It is yet another object of the invention to produce a monitoring systembased upon a network of continuously monitored nodes that arepermanently installed and whose collected data is synched in time andgathered automatically.

It is yet another object of the invention to provide integratedtransient voltage surge suppression.

It is yet another object of the invention to produce an analysis methodthat introduces system volatility as a unique measure for the study ofelectrical variables.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of an electrical system with the presentinvention components installed.

FIG. 2 is a block diagram of a basic PQ-node.

FIG. 3 is a schematic diagram of PQ-Nodes and the wireless communicationwith a multi-channel recorder.

FIG. 4 is a detail showing the PQ-node network transmission to the MCR.

FIG. 5 is a detail showing the PQ-node network transmission from theMCR.

FIG. 6 is a graphic representation of the PQ-Node's data transmissionmodes to the multi-channel recorder.

FIG. 7 is a detail view of a basic data block format common to allPQ-nodes.

FIG. 8 is a graph of a portion of a load cycle.

FIG. 9 is a detail view of a portion of FIG. 8.

FIG. 10 is a graph showing the data associated with data block 1 of FIG.9.

FIG. 11 is a graph showing the I-RMS standard deviation for data block 1of FIG. 9.

FIG. 11a is a graph showing the I-THD and power factor for data block 1of FIG. 9.

FIG. 12a is a graph showing the I-RMS standard deviations for the datablocks shown in FIG. 9.

FIG. 12b is a graph showing the power factors for the data blocks shownin FIG. 9.

FIG. 12c is a graph showing the I-THDs for the data blocks shown in FIG.9.

FIG. 13a is a graph showing the IRMS standard deviations and multiplierlimits for the data blocks of FIG. 9.

FIG. 13b is a graph showing the power factor and multiplier limits forthe data blocks of FIG. 9.

FIG. 13c is a graph showing the I-THD and multiplier limits for the datablocks of FIG. 9.

FIG. 14 is a graph of the predictive analysis region in relation to thethreshold value and the normal operating value.

FIG. 15 is an example of the trended RMS current moving into thepredictive analysis region.

FIG. 16 is a graph of the timing of the formation of time block and theformation of a data block within the time block.

FIG. 16a is a graphic and textual explanation of a data blockcompression cycle.

FIG. 16b is a graphic and textual explanation showing how new values areadded to a data block compression cycle.

FIG. 17 is a graphic and detail of PQ-node data blocks being linked to acommon timing structure.

FIG. 18a is graphic and textual explanation the first portion of aPQ-node data block transmission cycle.

FIG. 18b is graphic explanation the latter portion of a PQ-node datablock transmission cycle.

FIG. 19 is an example of the analysis system's software graphical userinterface representing the graphical representation of a powerdistribution circuit.

FIG. 20 is an example of the analysis system's software graphical userinterface representing the graphical representation of a portion of thecircuit of FIG. 19 with the data fields for a monitored load filled inwith sample data.

DETAILED DESCRIPTION OF THE INVENTION

Typical power quality monitoring uses predetermined static values asthresholds to alert of a possible problem. The trouble with this methodis that waiting for a threshold to be crossed by some electricalvariable before being alerted that a problem exists, is being informedthat a problem exists after it already exists. While this may beinformative, it's not predictive.

The term volatility, as it is used here, is defined as the measureddeviation of a variable form a determined normative value.

In power quality analysis, voltage and current are the two mostimportant variables. In this system, the voltage is considered theindependent variable in contrast to the current, which is considereddependent on the voltage and the impedance of the load.

Voltage is a variable that is regulated, as it is a measure of theelectromotive force supplied by a serving utility. The American NationalStandard for Electric Power Systems and Equipment, ANSI C84.1, is thestandard that regulates nominal service voltage ranges supplied tocustomers. This standard allows for variability of a plus or minus 5% ofthe nominal system voltage. Using this standard as an example, thevolatility of the voltage variable can be expected to be somewherebetween 0 and 5%, and will change throughout the day as costumers addand subtract loads and the utility responds by performing voltageregulation.

The need for the utility to perform periodic voltage regulation is inresponse to line losses that are incurred as a result of deliveringpower to the costumer or end user. Hence, voltage fluctuation, or thevolatility of the voltage can be a useful measure for determining thestiffness or robustness of an electrical distribution system.

Instead of merely measuring the voltage and comparing it to somethreshold, the instant invention analyses the relative volatility of thevoltage over time. The relative volatility can be put in terms of astandard deviation. Measuring the standard deviation of the voltage forprescribed time periods, at different places on an electricaldistribution system simultaneously, and comparing them provides a meansto identify any developing weakness in the electrical distributionsystem.

In the instant method, the standard deviation of the voltage becomes ameasure, which is measured over time. An increase in the volatility ofthe volatility of a variable over time is an indication of a growinginstability from a healthy system. In this way, the measured change involatility becomes the first indication of a problem, and is animportant key to predicting a fault condition. Studying the rate ofchange to this measure over time, is used to estimate the amount of timethat will elapse before user defined thresholds are crossed.

A study of current signatures is also a study of the impedancecharacterizations of a load. The study includes three basic operatingstates. The first state is when the load is first energized or turnedon, the second is when the load has reached a stable operating point,and the last is when the load is turned off.

Measuring the current elements, (i.e. RMS current, power factor, THD,etc.,) over these three parts of the load's operating cycle andrecording them, cycle by cycle, and applying the standard deviationmeasure to the different elements, defines the inherent volatility ofthe system under normal operating conditions. Comparing the standarddeviation of these values phase to phase and over time provides acomplete picture of the overall health of the system. In using thissystem it is not necessary to know what kind of loads are connected. Ifthe values for the RMS current and the power factor in one of the phasessupplying the load begin to drift beyond the historical standarddeviation measured for the three phases of RMS current and the threephases of the power factor taken together, then a problem with the loadis indicated. The load should be pulled from service and checked.

Used in this way, standard deviation can provide the earliest warning ofa possible problem.

Volatility is also be used for data compression. The standard deviationof the RMS voltage and RMS current waveforms, in the preferredembodiment, are derived over a 12-cycle period (based on a 60 cycle persecond power system). These two measures define the relative volatilityof that group of measured electrical values referred to here as a datablock. As discussed below, data blocks are formed continuously, oneafter another, and can be merged or compressed, as discussed below,based upon the measured standard deviations.

To access these parameters, from the data blocks and analyze them, asystem of collection nodes, a receiver and recorder and a computersystem are needed. FIGS. 1-12 show details of the systems describedbelow.

This system can best be described as an electrical monitoring and faultprediction system and is composed of three basic parts. The first partof the system is composed of Power Quality Nodes, or PQ-Nodes (see FIGS.1 and 2. The second part of the system is made up of multi-channelrecorders and the third or final part of the system is the SystemNetwork Server. A basic system has at least 5 PQ-Nodes, (based upon athree phase electrical distribution system, one PQ-Node for each phaseand neutral point monitored at the distribution panel supplying themonitored load and at least one PQ-Node for monitoring at a single phasemonitored load.) one multi-channel recorder and an Ethernet connectionto a server. A functional description of each of these parts of thesystem is given below.

FIG. 1 is a power system that has the components of the systeminstalled. At the top left of the diagram, the utility service entrance100 is shown. This line leads to a main service panel 101. In thissystem, two sub panels are fed from the main distribution panel 101. Oneis a power distribution panel 102 and the other is a motor controlcenter 103. Three 30 hp motors 104 are connected to the motor controlcenter 103.

Three transformers 105 are attached to the power distribution panel 102as shown. These transformers serve four branch circuit panels 106. Thebranch circuit panels 106 serve a number of loads 107. One of the branchcircuit panels feeds an Uninterruptible power supply (UPS) 108 thatfeeds one of the branch circuit panels 106 a, which then feeds otherloads.

All of these devices are well know in the art. The difference is the useof the invention components, as discussed below.

The first of these is a PQ-Node. A PQ-Node is a multi-channel recorderthat measures the single-phase voltage and corresponding single-phasecurrent at the point where it is installed on an electrical distributionsystem. In FIG. 1, PQ-Nodes 10 are found distributed throughout thesystem as shown. Other components include a Multi-channel recorder 20,system network hubs 30, a network server 40 and an access terminal 50,also known as a base station. The base station has software, which is ameans for analyzing said plurality of data blocks. All of thesecomponents are discussed in detail below.

The PQ-Nodes contain a number of components. FIG. 2 is a block diagramof a basic PQ-node. Note that each PQ-Node is placed in series with aspecific component of the system. Thus, it has an input side whereground, neutral, and line terminals are provided. The PQ-Node circuitrycan be protected by transient voltage surge suppression modules 11, asshown. A power supply 12 then provides DC power to the PQ-Node asneeded. The voltage channel measures voltage with respect to thegrounded conductor or ground. The current is measured via a Hall effectsensor 13, the voltage and current are then filtered and conditionedthrough a low pass filter 14 and a signal conditioner 15. Amicroprocessor 16 and ram 17 are provided, as discussed below. Output ofthe PQ-Node is passed through buffers 18 to an alarmtransmitter/receiver 19 a and a data logger transmitter/receiver 19 b,also as discussed below.

The PQ-Node initially samples the voltage and current simultaneously at153.6 kHz, or 2560-samples per cycle, per channel with 16-bitresolution. This provides a reasonable level of transient analysis, aswell as performing a more accurate determination of the zero crossingpoint and the peak measurement value of the waveform. Once the waveformshas been sampled and stored in a register 17, they are down sampled by afactor of ten to one, to 256 samples per waveform, starting with thefirst zero crossing sample identified and continuing from there onthrough 12 complete cycles, or 24 half cycles. See, FIG. 16. It thenconverts the sampled data into electrical values, via a microprocessor16, averages those values over the 12 cycles measured and stores thosevalues into what is referred to here as a data block.

Data blocks are then stored in a comparator register 17 of the PQ-Nodeand based upon the relative stability or volatility of the measuredelectrical values are either sent through a compression cycle whereconcurrent data blocks are merged or the data blocks are sent to theoutput register where they are prepared for transmission to themulti-channel recorder. This means that the data blocks will be either12 cycles, (200-miliseconds) in duration or longer due to compression. A200-milisecond data block indicates that volatile voltage and or currentconditions were measured. Compressed data blocks indicate relativeperiods of electrical stability. The fundamental goal of the PQ-Node isto segregate the volatile periods, where the voltage and/or currentvalues are in relative flux from the stable periods, where the voltageand current values are relatively static over time.

All data blocks, while in the PQ-Node, are stored in nonvolatile memory17. The PQ-Node derives its power for operations from the circuit, butalso has a small battery 12 a on board to allow it to operate when poweris lost. The battery provides enough power to complete the last datablock, the one formed during the 200-miliseconds when power was lost,and move it to a nonvolatile storage register.

A PQ-Node also a means for transmitting the data blocks to themulti-channel recorder either wirelessly or through hard wire connection(e.g., Wi-Fi or RJ-45 connection).

A PQ-Node transmits data to the multi-channel recorder in two possibletransmission modes. One mode, the data log mode (using the data loggertransmitter/receiver 19 b), is dedicated to the transmission of the datablocks from the PQ-Node to the multi-channel recorder, with thetransmission of this data initiated by the PQ-Node, based upon thevolatility of the data being collected, as explained below. This mode oftransmission occurs on a common transmission frequency shared by all theother PQ-Nodes on the network.

The other mode of transmission, called the alarm mode, is initiated bythe multi-channel recorder and is sent through the alarmtransmitter/receiver 19 a. This mode of transmission provides themulti-channel recorder 20 the ability to poll specific data blocks fromany PQ-Node on the network at any time, without waiting for the PQ-Nodeto initiate transmission. What this does is allow the multi-channelrecorder 20 to poll individual data blocks from a PQ-Node before thedata block enters the data block compression cycle. This feature iscrucial to the multi-channel recorders ability to compare data fromdifferent PQ-Nodes collected in the same data block period of time. Thealarm Mode is also used to transmit the voltage and current waveformsignatures that exceed the alarm thresholds set by the user. Thisfeature is crucial in the tracking and determination of the origin of atransient as it enters and moves through the electrical distributionsystem. Both transmission modes are full duplex modes of transmission.

The next component of the system is the multi-channel recorder 20. Thisis a basic computer with large permanent storage (e.g. hard drive, flashmemory etc.) and a wireless network interface card. It connects directlyto the servers 30, via an Ethernet connection. It communicated with thePQ-Nodes via the wireless network card

The multi-channel recorder 20 receives the electrical values measured bythe PQ-Nodes 10 in the form of data blocks, explained below. Themulti-channel recorder houses a data base, and is responsible forgrouping and sorting the data blocks from multiple different PQ-Nodesinto predefined groups, based upon their relative location on thedistribution system. It separates the volatile data blocks—those thatare only 200-miliseconds long-from the stable or compressed data blocksof longer time durations. In this way, the database creates andmaintains the relative historical volatility indexes used in themonitoring and fault predictive analysis. This fault predictive analysiscan be broken down into three basic groups: transient analysis, circuitor distribution analysis and load analysis. An explanation of each isdetailed below.

The proposed predictive analysis method employs a combination ofhistorical trend analysis and comparative analysis, comparing datablocks produced by multiple PQ-Nodes in the same block of time. Becausethe comparative analysis methods performed by the multi-channelrecorder's database are time sensitive, time keeping functions betweenthe multi-channel recorder and the PQ-Nodes are of a fundamentalconcern. The synching of the master clock in the multi-channel recorderwith the individual clocks in the PQ-Nodes is maintained by precisiontiming protocols as explained in IEEE Publication 1588. This synchingand calibration is part of every data block transmission that occursbetween the PQ-Node and the multi-channel recorder and is crucial topredictive fault analysis.

The multi-channel recorder 20 is also responsible for performing theFourier analysis on the strings of sampled values that it receives fromthe PQ-Nodes, called voltage blocks and current blocks, when those nodesgo into a compression cycle. (The voltage and current blocks areexplained below.) The load analysis is performed via techniques relatedto current signature analysis and hence the need to analyze the currentsignature in the frequency domain.

All of this capability is in software stored in the multi-channelrecorder, which can be considered means for analyzing said plurality ofdata blocks.

The next component is the system network server 30, the main interfacebetween the user, the multi-channel recorders 20 and the PQ-Nodes 10,either via a computer terminal, a laptop or other computer peripheral.It provides the means for this system to become enterprise wide, byconnecting the multi-channel recorders 20 and nodes 10 in one facilitywith other systems in other facilities. It provides for direct useraccess for the setting of various defined user thresholds and for realtime data block or transient signature retrieval.

It has the means to correlate data from multiple different multi-channelrecorders from different facilities. It has the ability to automaticallyproduce fault prediction warnings and preventative maintenance reports.It also has the ability to periodically and automatically generateenergy audit reports. These audits would describe the energy consumptionof each load monitored as well as the total load of the building orfacility. It should compute the monthly cost for the electrical powerconsumed by each load including power factor and demand penalties ifany, and what percentage of the total cost they represent.

The system server should also provide capacity for long-term storage ofdata blocks that have historical significance. It should create andmaintain a library of recorded fault conditions to be used forcomparison and identification of future anomalous electrical conditions.

FIG. 7 is a detail view of a basic data block format common to allPQ-nodes.

The electrical values measured, (i.e. voltage and current) are measuredover a 10/12 cycle, (200-milisecond) at 50/60 HZ, interval time varieswith actual frequency.

As noted above, each PQ-Node initially samples the voltage and currentsimultaneously at 153.6 kHz, or 2560-samples per cycle, per channel with16-bit resolution. This provides a reasonable level of transientanalysis, as well as performing a more accurate determination of thezero crossing point and the peak measurement value of the waveform. Oncethe waveforms has been sampled and stored in a register, they are downsampled by a factor of ten to one, to 256 samples per waveform, startingwith the first zero crossing sample identified and continuing from thereon through 12 complete cycles, or 24 half cycles (see FIG. 16). It thenconverts the sampled data into electrical values, via the microprocessor16, averages those values over the 12 cycles measured and stores thosevalues into what is referred to here as a data block. A data block 50 isa collection of measured values sampled and recorded over a specifiedperiod of time. This group of recorded values includes but not belimited to, typical electrical measurements such as voltage 51 andcurrent values 52, temperature 54, power factor 55 and any otherpotentially useful measure that is used in the analysis of a dynamicsystem.

The measured values are aggregated using the square root of thearithmetic mean of the squared input as specified by the IEC 61000-4-30standard. The following is a basic description of each electrical valuemeasured and recorded in the data block:

PQ-Node ID: This identifies the PQ-Node that created the data block;

Voltage Time Stamp: this is the actual time of the first zero crossingof the 10/12 concurrent cycles of voltage signatures that areindividually sampled and measured;

Max. Peak voltage: this is the maximum peak voltage measured during thetime period covered by the data block;

Min. Peak voltage: this is the minimum peak voltage measured during thetime period covered by the data block;

Delta V-RMS: this value is the difference in voltage between the lastcycle recorded in the data block and the first cycle recorded in thedata block. It will have either a Negative, (−), or Positive, (+),value;

Mean Avg. V-RMS: this is the average RMS-voltage recorder over the timeperiod covered by the data block;

Max V-RMS: this is the maximum RMS voltage value recorded during thespan of time represented by the data block;

Min V-RMS: this is the minimum RMS voltage value recorded during thespan of time represented by the data block;

V-RMS Std. Dev. this value is the standard deviation, expressed as apercentage, of the RMS voltage over the time period covered by the datablock;

Hz: this is the average period of the voltage waveform over the timeperiod covered by the data block;

Current Time Stamp: this is the actual time of the first zero crossingof the 10/12 concurrent cycles of current signatures that areindividually sampled and measured;

Max. Peak current: this is the maximum peak current measured during thetime period covered by the data block;

Delta I-RMS: this value is the difference in current between the lastcycle recorded in the data block and the first cycle recorded in thedata block. It will have either a Negative, (−), or Positive, (+),value;

Mean Avg. I-RMS: this is the average RMS-current recorded over the timeperiod covered by the data block;

I-RMS Std. Dev: this value is the standard deviation, expressed as apercentage, of the RMS current over the time period covered by the datablock;

Source Impedance: the source impedance is derived by dividing theDelta-V by the Delta-I. This value is measured in ohms and is a measureof how much power is lost to the electrical distribution system while itis conveying power to the load;

Watts: this value is the product of the average RMS voltage, the averageRMS current and the Power Factor;

Joules (Watt seconds): this is a measure of the electrical energyexpended over the time period covered by the data block;

Power Factor: this is a measure of the Cosine angle between thefundamental voltage waveform and the fundamental current waveform;

Accumulated Time: this is the length of time that the data blockrepresents. It has a 200-milisecond minimum value; and

Data block #: this identifies the data block as to what segment of thehour it was created. (1-18000).

All of these values are analyzed as part of a load cycle analysis. Aload profile for a load is constructed by averaging the electricalvalues of that load over several load cycles. A load cycle is thatperiod of time starting with when the load is initially turned on andcontinues until it is turned off or disconnected. A load cycle isdefined by the automated predictive analysis method as a collection ofconcurrently created data blocks, whose I-RMS values exceed thecircuit's ambient noise levels. A load cycle begins with one or moresingle data blocks whose Delta I-RMS value goes positive and whose valueexceeds compression limits. The load cycle's I-RMS value then arrives atsome relatively stable point where data block compression can begin. Theload cycle continues, as a collection of sequentially created compressedand single data blocks, depending on load stability, until ending with asingle data block whose Delta I-RMS is negative, and whose I-RMS valuereturns to the circuit's ambient noise level. See FIG. 8, whichillustrates a portion of a load cycle showing the variations in RMScurrent over time and FIG. 9, which is a detail view of a portion ofFIG. 8. Each of the segments shown is a data block. As shown, time isthe X-axis. These figures will be discussed in detail below.

Another crucial aspect of the load profile analysis is that, as notedabove, the system is based upon a network of continuously monitorednodes that are permanently installed and whose collected data is synchedin time and gathered automatically. This means that every PQ-Node hasits own clock that must be synched to the multi-channel recorder that itis addressed too. Each multi-channel recorder has its internal clocksynched to the network server. This network of distributed clocks iskept synchronized via precision timing protocols such as those outlinedin IEEE Standard-1588, Standard for A Precision Clock SynchronizationProtocol for Networked Measurement and Control Systems.

The fundamental unit of time for this system is the 200-millisecond timeblock. In this system, each hour of the day is broken up into 18,000,sequentially occurring, 200-milisecond blocks of time. The first blockof time is created at the top of the hour, and is referred to here astime block #1. It is initiated at 00′:00″:00′″ and completed at00′:00″:20′″. The second time block is initiated at 00′:00″:20′″ andcompleted at 00′:00″:40′″ and so on through the 18000^(th) time block,which is initiated at 59′:59″:80′″ and completed at 00′:00″:00′″. Thissequential cycle for the creation of 18,000, 200-millisecond blocks oftime per hour, or time blocks, begins all over again at the top of a newhour (see, e.g., FIG. 17). This timing structure is fundamental to howthe network of distributed PQ-Nodes collects data. All PQ-Nodes on thenetwork are synched together and act in concert, sampling and measuringthe various electrical values, locked in step with this fundamentalcycle of time. The period of 200-milleseconds was selected for the timeblock duration because it provides a common timing factor for both the50-Hz electrical systems, (10 cycle duration), used in Europe, and the60-Hz electrical systems, (12 cycle duration), used in the UnitedStates.

Data Block Timing:

As noted above, a data block is a collection of measured electricalvalues taken over a 10 cycle, (for 50-Hz), or 12-cycle, (for 60-Hz),time period (see also, FIG. 7). This time period correlates to the200-millisecond time block structure explained above. Data blockformation begins with the first zero crossing of the voltage and currentsine waves occurring in a time block and lasts for 10/12 concurrentcycles. See FIG. 16 for clarification:

Each 10/12-cycle time period of aggregated measured electrical valuesconstitutes a single data block of information. As each hour of the dayis broken up into 18,000 blocks of time, or time blocks, there are aswell, 18,000 sequentially created data blocks. One data block createdfor each time block.

FIGS. 8 and 9 graphically represent a load profile of I-RMS. FIG. 9 is adetail of a portion of FIG. 8 showing enlarged detail. In this figure,there are single data blocks (DB-A-DB-F) and compressed data blocksDB#1-DB#7. The data block values used in defining the operationalcharacteristics for a load cycle, and consequently, the construction ofa load profile are taken from the current half of the data block and arelisted below for clarity. As discussed below under data blockcompression, the data blocks in FIGS. 8 and 9 are explained as follows.All load profiles begin with a single data block. As data isaccumulated, steady state periods are compressed (again, as discussedbelow). Such data blocks are accumulated until conditions change atwhich point, another single data block is captured. Once again, after asteady state is achieved, the data blocks compress again. This isclearly shown in FIGS. 8 and 9.

There is one important caveat to consider when monitoring load cyclesfor the creation of a load profile. That is that the load cycles shouldbe representative of a healthy load at an initial stage. A loadcalibration of some kind should be performed at the time of the initialmonitoring to insure that the load is functioning correctly and withinacceptable limits. This ensures that the initial load cycles monitoredand the subsequent load profiles developed are reflective of a healthyload and suitable for baseline comparisons to future load cycles.

Although FIG. 9 shows I-RMS values, the load profile is developed forall of the values in a given data block (see FIG. 7).

Values taken from current part of data block are:

Max Peak current;

Average I-RMS;

I-RMS standard deviation;

Power factor; and

Compression length in time blocks (Accumulated Time).

In addition to the above values used to describe a load, the current'sTotal Harmonic Distortion (THD) is included. This value is derived byanalyzing the sampled current waveform values contained in the currentblock that was transmitted to the multi-channel recorder's databaseduring data block compression. The multi-channel recorder's databasetakes these sampled waveform values and calculates a fast Fouriertransform to determine the current's THD, at that current level.

Once a load cycle begins, the multi-channel recorder's database takesthe electrical values in the current half of the compressed data blocks,plus the current's THD and begins to calculate the load cycles baselinevalues. These values are used to define the load's operationalcharacteristics and are listed below.

Four values that define the operational characteristics of a load cycle:

load cycle average RMS current, (I-RMS) and its standard deviation;

load cycle average power factor and its standard deviation;

load cycle average current THD and its standard deviation; and

load cycle average data block I-RMS standard deviation and its standarddeviation.

As touched upon earlier, the operational characteristics of anelectrical load are fundamentally defined by how the load draws currentfrom a source of electrical supply. Measuring the three basic currentelements, the RMS current, power factor and current THD, over the courseof a load cycle and relating these values to each other describes theinherent qualities that characterize that load.

The fourth value used in defining the operational characteristics of aload is the I-RMS standard deviation value measured for each compresseddata block contained in the load cycle. As explained earlier, this is ameasure of the average variance of the RMS current measured for thatdata block and quantifies its volatility. This value is averaged overtime and used as a gage to denote the relative stability or volatilityof the load in how it draws current during a load cycle. A suddenincrease in this value would indicate an increase in the volatility ofthe current and indicate a potential disequilibrium occurring within theload. FIG. 10 shows an example of how the current standard deviation canbe used when examining data block #1 of FIGS. 8 and 9. In this case, thevertical component is time (measured in time blocks). The averagecurrent for this data block is measured at 10.5 amps. (See FIGS. 8 and9). For a current standard deviation of 2.3%, there is an envelope 60(shaded block) of between 10.26 and 10.74 amps. Thus, if the I-RMSexceeds these values, a warning is issued. The warning and alarm phasesare discussed at length below. FIG. 11 is a graph showing the I-RMSstandard deviation for data block 1 of FIG. 9 A data point at 2.3%).

Tying the power factor value, the current THD value and the I-RMSstandard deviation value to their corresponding RMS current valueprovides a common frame of reference by which the relationship betweenthese four variables can be quantified. Their average values, over thespan of the load cycle, are used to determine the standard deviation foreach variable, which is used in defining its corresponding operationalboundary and thereby determining threshold values. This is explainedbelow. FIG. 11a is a graph showing the I-THD and power factor for datablock 1 of FIG. 9. Here, the I-THD of 12.4% and the power factor of83.8% are shown as data points for data block #1.

In the same way, the data points are derived for the other data blocksfrom FIG. 8. FIG. 12a is a graph showing the I-RMS standard deviationsfor the data blocks shown in FIG. 9. This shows a relatively lineargraph over the load profile. Similarly, FIG. 12b is a graph showing thepower factors and FIG. 12c is a graph of the I-THD for the data blocksshown in FIG. 9. In the same manner, data points can be generated forany desired value from any particular load profile.

Taking the average I-RMS value of each of the compressed data blockscontained in the load cycle and multiplying it by its respective datablock length, in time blocks generates a load cycle average I-RMS. Theseindividual values are then added together, the total of which is thendivided by the number of compressed data blocks contained in the loadcycle to derive the average I-RMS value for that load cycle. Once theload cycle's average RMS current value is known, its standard deviationvalue is calculated. The object of this is to derive an operationalboundary for the load cycle RMS current.

FIG. 13a is a graph showing the IRMS standard deviations and multiplierlimits for the data blocks of FIG. 9. Note that the upper and lowerlimits shown are the operational boundary for the load cycle RMScurrent. Similarly, FIG. 13b is a graph showing the power factor andmultiplier limits for the data blocks of FIG. 9 and FIG. 13c is a graphshowing the I-THD and multiplier limits for the data blocks of FIG. 9.

Using the standard deviation to derive the operational boundary for thevariable in question permits the system being monitored to define itselfrather than using some predetermined static value. This is importantbecause each load must be considered as unique if accurate load profilesare to be developed that define the operational boundaries of the load.The standard deviation also facilitates the ability to automate thisfunction because the operational boundary of a load defines thethreshold values that are used to drive the automated predictiveanalysis method.

Adding a multiplier to the standard deviation number creates a means forthe automated predictive analysis method to automatically manipulate thesensitivity of the system. This allows for the possibility to activelycontrol, by widening or narrowing the width of the operational boundaryand thereby adjusting the corresponding threshold values. This processacts much like the focusing of a lens, allowing the analysis method theability to adjust the sensitivity of the monitoring system in responseto the changing analytical needs that circumstances may dictate.

As an example, the standard deviation multiplier for the RMS current isset at 2, which would set the operational boundary used by the loadanalysis to the 2^(nd) standard deviation value. This would create anoperational envelope that encloses approximately 95% of the compresseddata blocks contained in the load cycle, based upon the Gaussiandistribution model. This means that, at this setting, less than 3% ofthe load cycle's data blocks should fall on either side of thisoperational envelope. This 3% equates to a period of time, since datablocks are a collection of time blocks.

Looked at in this way a percentage of a load cycle's data blocksrepresents a period of time as well as a quantity of the variable.Relating the quantity of a variable to a percentage of a load cycleprovides a means for time to be used as a qualifier in the determinationof a system's response to a variable's movement beyond a thresholdvalue. Adjusting the threshold value would automatically adjust theamount of time used as a qualifier. Referring to this example, an RMScurrent value that exceeds the 2^(nd) standard deviation boundary mustalso exceed it for more than 3% of the total load cycle duration in timeblocks to activate a notification response by the system.

The load cycle's average power factor, average current THD, and averagedata block I-RMS standard deviation with their respective standarddeviation values are calculated in the same way as the load cyclesaverage I-RMS explained above. The intent is the same as above, toautomatically establish their respective operational boundaries anddefine their threshold values. The standard deviation multiplier foreach variable can be independently set and automatically controlled bythe system. This provides the means to automate the control of therespective threshold values, there by adjusting the sensitivities of thesystem automatically in response to changing environmental factors.

The relationship that exists between a load's instantaneous RMS currentvalue and its corresponding power factor, current THD and the data blockI-RMS standard deviation values are important and must be appreciated tounderstand how a load profile is developed. This relationship is definedduring the formation of each compressed data block created during a loadcycle, when a particular value of a load's RMS current is associatedwith a corresponding value for power factor, current THD and the I-RMSstandard deviation. The automatic creation of compressed data blocksover the span of the load cycle provides the system with the informationrequired to develop the operational boundaries that determine thethreshold values for each load cycle.

The load cycle's power factor, current THD and the data block I-RMSstandard deviation values are related to and dependent upon the loadcycle's I-RMS value. That is to say, for every I-RMS value, there isassociated a particular power factor value, a current THD value and adata block standard deviation value.

While a load cycle's power factor threshold limit is the same percentagevalue over the span of a load cycle, the power factor value associatedwith 4-amps of RMS current may be quite different than the power factorvalue associated with 10-amps of current. Because the threshold limitsare defined based upon the standard deviation of a variable, given as apercent of variance, times a multiplier, the actual limiting values of avariable is dependent upon the value that is associated with aparticular RMS current value. For example, the power factor limit forthe load profile in the above example is +3.8%. The power factorassociated with 4-amps of RMS current is 82%. This means its operationalboundaries or limits are defined as 82%±3.8%. This equates to an upperboundary of 85.1% and a lower boundary or limit of 78.9%. Each of theseassociated values must fall within the acceptable threshold limitsdetermined by their operational boundaries or the automated predictiveanalysis system initiates a programmed response.

Fault prediction demands the development of indicators that forewarn ofpotential problems prior to their occurrence. The use of standarddeviation to quantify the volatility of the voltage and current valuesmeasured over time is meant to be such an early warning indicator. Thisis based upon the premise that electrical systems are dynamic systems;they must achieve some reasonable degree of equilibrium to sustain theiroperation. The average measured variance with respect to time of thevoltage and the current variables for an electrical load describes thatsystem's unique state of equilibrium by quantifying itsvolatility/stability. The quantifying of the equilibrium thatcharacterizes an electrical load is expressed as an indicator based uponmeasuring the load cycle's stability called the load cycle stabilityindex.

The load cycle stability index: =stability metric %−avg. load cycleI-RMS standard deviation %.

The stability metric % is calculated by taking the number of time blocksrepresented by the compressed data blocks contained in the load cycleand dividing that number by the total number of time blocks contained bythe load cycle.

The load cycle average I-RMS standard deviation is used for the avg.standard deviation % in the above equation. Using the above load cycleexample for FIGS. 13a-13c , the stability metric % is equal to 3,846compressed time blocks/3974 total time blocks, ×100, which equals 96.78%(compressed time blocks are discussed in greater detail below).

Subtracting the avg. load cycle I-RMS standard deviation of 22.5% fromthe Stability Metric of 96.78% produces a load cycle stability index of74.28%. Because a load profile is comprised of several load cycles, theload cycle stability index for a load profile becomes an average valuewith a standard deviation. This develops the operational boundaries forthis indicator.

One problem is that the greater the advance warning such an indicatorprovides, the greater the likelihood that it will produce somepercentage of false alarms. It is the nature of dynamic systems toexperience transient conditions during the normal course of anoperational cycle that can trigger early warning indicators. For thisreason, such an indicator tends to generate an alert instead of awarning or an alarm.

For each load cycle there are maximum and minimum values. These valuesare taken from the single and compressed data blocks that make up a loadcycle (see FIGS. 8 and 9). They simply indicate the maximum and minimumvalues recorded for the peak and the I-RMS current measured during theload cycle. The maximum peak current and I-RMS values are typicallymeasured during the initial start up of the load and are thereforegenerally contained in the first data block of the load cycle, which isa single data block most of the time (again see FIGS. 7, 8 and 9).

As discussed above, a load profile is the result of taking theoperational characteristics that are defined by a load cycle, andaveraging them over several load cycles. This action is designed toproduce the historical baseline references that the automated predictiveanalysis is predicated upon. The load profile includes the four mainelements that define the operational characteristics of a load cycle, asdescribed above, plus the load stability index described above. The listis included below for clarity:

Five Elements that define the operational characteristics of a loadprofile:

the load profile average RMS current, (I-RMS) and its standarddeviation;

the load profile average power factor and its standard deviation;

the load profile average current THD and its standard deviation;

the load profile average data block I-RMS standard deviation and itsstandard deviation; and

the load profile stability index.

The load profile's stability index calculated above does not reflect astandard deviation or limit value because the example load profile ismade up of only one load cycle. Multiple load cycles are needed toproduce the standard deviation and associated limit value for this loadprofile element.

The load analysis provided by the automated predictive analysis systemproduces three levels of automated response and/or notification. Theyare listed here in order of their status of severity:

Alarm status level: the most severe status level, indicating animmediate need for response to a system problem. This threshold isdefined as outlined in the predictive analysis region section below.

Warning status level: a reduction in the level of severity as comparedto the alarm level. This indicates that there is some amount of timethat exists before the event reaches the alarm level. This thresholdlevel is also defined in the predictive analysis region section below.

Alert status level: this level is intended to provide early notificationof conditions occurring that support the potential for future warningsor alarms.

The avg. I-RMS element of a load profile defines the standard deviationenvelope that is used to determine the boundaries of the predictiveanalysis region. Because of this, the limits derived by this element areresponsible for the initiation of all alarms and warnings generated bythe automated predictive analysis system with regards to load analysis.

The other four elements of the load profile (see above) are responsiblefor generating the various alerts that notify the end user of potentialproblems that are developing with regards to the load.

The limiting values produced by a load profile's power factor, currentTHD, data block I-RMS standard deviation and load cycle stability indexcreate the possibility for several kinds of alerts, with differentlevels of relative urgency. For example, a sudden change in power factorof a load beyond its limiting value could be considered more meaningfulthan the change in the load cycle stability index. As well, the movementof two or more of the load profile's elements beyond their thresholdlimits would be more meaningful than if just one element did.Ultimately, the characteristics of the load must be taken intoconsideration when developing a hierarchy for these different alerts.The many different possible combinations provided by these four loadprofile elements creates the potential for characterizing the unique waythat a load responds to conditions that ultimately drive it to alarmstatus. Over time, the accumulation of data for a given system providesinformation that is unique to that system and provides the system withthe ability to predict problems for that system.

FIG. 14 is a graph of the predictive analysis region in relation to thethreshold value and the normal operating value. This graph shows thedifferent regions of operating values that are used to issue the variouswarnings and alarms. The values for these limits are determined by thespecific parameter being measured. The graph is a template havingmagnitude of a parameter on the vertical axis and time as the horizontalaxis. A horizontal line labeled “Initial average value” (in this caseI-RMS current) is placed at the appropriate level on the vertical axis.Two lines around this normal operating value represent the standarddeviation from that normal value (+/−). At a determined point above thehighest initial standard deviation is a dashed line. The space betweenthese two lines is labeled the “warning region”. Above that is anotherdashed line that marks the threshold value for the alarm region.

Thresholds represent operational boundaries and for that reason tend tobe static or fixed in nature. Using the standard deviation to quantifythe stability or volatility of an electrical variable allows for theearliest possible notification of a change in the normal operatingparameters as defined by that electrical variable. The range or regionbetween the upper boundary of the standard deviation envelope and thethreshold value provide a period of time to analyze and determine themeaning of a change in operational states of an electrical variable.Once analysis is complete, predictions can be made.

Electrical variables tend to be dynamic and may have many operationalstates. For this reason, a warning region (and as discussed even analert region when needed) is used, which defines the bottom half of thepredictive analysis region. This warning region is an acknowledgmentthat electrical variables, will occasionally vary beyond the standarddeviation envelope and into the predictive analysis region. Most of thetime this is a transient condition and the variable moves back to adoptits normal stable measure. The warning region also acts as a buffer zoneto prevent spurious false alarms. Moreover, the warning region providesthe space for the change in the trend of a variable to be confirmed. Thepredictive analysis method is designed to provide a complete analysisand a fault prediction prior to the variable moving into the alarm partof the predictive analysis region.

FIG. 15 is an example of the trended RMS current moving into thepredictive analysis region. Here, the change in current over time(ΔI/ΔT) is plotted. A single occurrence of the AI/AT moving into thewarning region produces an alert. A continued movement or repeatedincursions into the warning area generates a warning. A move into thealarm region obviously produces an alarm. However, as discussed below,one of the unique advantages of this system is that it not only detectswarning and alarm levels, it is also able to predict failure using trendanalysis, as discussed below to forecast a time to overload or failure.

The use of prediction, as is used herein, implies being notified that aproblem exists before the threshold that defines the problem is crossed.As discussed above, this system attempts to qualify the measured valuethat lies between the upper boundary of the standard deviation envelope,defined by the electrical variable and the threshold value (see FIG.14). An example of this process is described below, with reference toFIGS. 14 and 15.

Step 1: create user defined alarm threshold values for the monitoredload being monitored. These values are derived from the operating andperformance specifications for the monitored load in question,information that is supplied by the manufacturer. Additional thresholdsinclude NEC specified operational limits for the branch circuitsupplying the critical load as well as other industry acknowledgedoperational limits for the specific kind of load being served, (e.g.,NETA Acceptance and Performance testing criteria, etc.) These thresholdvalues define the operational limits for the monitored load and areprogrammed into the graphical user interface described above during thecommissioning and start-up of the monitored load. (See FIG. 13 for anexample using a motor as the monitored load.) All threshold values areprogrammed into both the PQ-Node and the multi-channel recorder.

Step 2: Establish normal operating parameters for the monitored loadbeing monitored. This is done by measuring and recording all pertinentelectrical values during the start-up and commissioning of the monitoredload. This includes every electrical element recorded in the data block.The standard deviation for each of the electrical values referred to inthe data block is also measured and recorded. The standard deviation ofthe electrical variable takes into account the impact that theenvironment can have on the electrical element being monitored overtime. These values are used as a baseline for the predictive analysisand functionally define what the stable operational state of themonitored load is.

Step 3: Subtract the normal value and the standard deviation value foreach of the electrical elements derived in step two from the alarmthreshold values for that electrical element defined in step one. Thisvalue represents the magnitude of the predictive analysis region. (SeeFIG. 14).

Step 4: The first warning level is reached when the average value of anelectrical variable moves beyond the standard deviation envelope andinto the predictive analysis region. As an example, the monitored loadin this case is a three-phase motor with a nameplate rating of 7.6-amps.The testing and monitoring performed at commissioning and start-upestablished a 7.5-amp average RMS current value for its normal operatinglevel. The initial standard deviation for this measure is 4.25%. Thismeans that the initiation of the predictive analysis region and thefirst warning level is when the average value of the RMS current for themotor moves beyond 7.82-amps.

The overload threshold for this example is established either bymanufacture's specifications or by the monitored load's branch circuitrating, whichever is least in value. For this example, the overloadthreshold is set by the National Electrical Code for overload protectionof a motor, which is 125% of the motor's nameplate rating, or 9.5-amps.The predictive analysis region is then defined as the area lying between7.82-amps and 9.5-amps (see FIG. 15). The magnitude of the predictiveanalysis region in this example is 9.5 minus 7.82, or 1.68-amps. Thefirst 50% of this region is designated the warning region and the second50% the alarm region. Using this example, the warning region extendsfrom 7.82-amps to 8.66-amps. The alarm region extends from 8.66-amps to9.5-amps. The warning and alarm values as well as the threshold valuesare stored in the PQ-Node as well as in the multi-channel recorder. Whenan electrical variable moves into the predictive analysis region, theinitial warning that is generated is generated in the PQ-Node inquestion, and the warning information is sent immediately to themulti-channel recorder via the alarm transmission mode described above.In this way, the multi-channel recorder is not waiting for the data tobe sent to it in the normal flow of data blocks transmitted via the datalogger transmitter, but instead receives the warning immediately, viathe alarm transmitter.

Step 5: Once an electrical variable moves into the predictive analysisregion and a warning is generated, the rate of change of this variableis tracked and updated continuously by the PQ-Node. The first predictiongenerated is that of an estimated time for the variable to reach thealarm region value, (in this case 8.66-amps), and the Overload Thresholdvalue, which in this case is 9.5-amps. The warning is displayed at thesystem terminal monitors (discussed below) and is updated at every 10%increase in the value of the electrical variable in question. Themonitor displays the monitored load with a warning/alarm status box andcolor bar demonstrating the severity of the warning. A green colorrepresents a system normal status. A warning status starts with thecolor yellow and rises to various deeper shades of orange until it hitsthe 50% level, which is the alarm region, where it turns red in color.This part of the analysis provides continuously updated predictions ofthe estimated time remaining before the electrical variable reaches thethreshold value and is performed by the PQ-Node, with the warnings andalarms being sent to the multi-channel recorder. All warning and alarmreports are saved in the multi-channel recorder in a report log that iscreated for each monitored load that this system is installed tomonitor. These reports become part of the historical record and are usedin future analysis of the monitored load.

Step 6: Once the multi-channel recorder has been alerted to the specificwarning by a PQ-Node that one or more electrical variables have movedinto the predictive analysis region, it begins polling data from theother PQ-Nodes that have been installed to protect the monitored load.It identifies the PQ-Nodes relevant to the analysis to be performedbased upon the installation of the PQ-Nodes into the graphical userinterface described below in the Electrical distribution Analysisdescription. (See FIG. 19.) In this example, the load in question is athree phase motor load, requiring the multi-channel recorder to poll theother PQ-Nodes monitoring the other two phases of the distributionsystem for comparison; both at the branch circuit and at the feeder andmain service. All PQ-Nodes installed in the graphical user interface forthat particular monitored load are polled and the data compared andanalyzed. (See FIG. 19)

The first priority for the multi-channel recorder is to isolate thebasic cause of the problem by determining whether the problem originatesin the electrical distribution system, the motor or in the mechanicalload that the motor drives. In this example, the multi-channel recorderfirst compares the latest source impedance values from all of thePQ-Nodes related to the monitored load in question to their respectivehistorical averages and in this case finds that all of the sourceimpedance values fall well within their established standard deviationsenvelope. The multi-channel recorder then compares the RMS voltagevalues measured at PQ-Nodes 3A, 3B and 3C, (see FIGS. 1 and 19), andfinds the average of the three phases to be 120.0 volts with a standarddeviation of 1.6%. This demonstrates a stable electrical distributionsystem and a fairly stable voltage supply. This indicates that theelectrical distribution system is not the cause of the problem.

The multi-channel recorder then compares the RMS current, the powerfactor and load impedance values measured at PQ-Nodes 3A, 3B and 3C tothe name plate data programmed into the software when the monitored loadwas commissioned. (See FIG. 19.) In this case, the fact that only one ofthe phases went into alarm is indicative of an imbalance in the motor.Comparisons of the RMS current, the power factor and the load impedancefor PQ-Nodes 3A, 3B and 3C reveal the probable cause for the initialhigh current on the one PQ-Node. (3A is the PQ-Node in alarm for thisexample) See table-5 below. The higher power factor and lower impedancemeasured in PQ-Node 3A is indicative of a stator winding that isbeginning to fail, or shorting turn to turn. This reduces the inductivecomponent in the winding, reducing the impedance, raising the powerfactor and consuming more power in the form of heat lost in the winding.

TABLE 1 Motor Overload Analysis Example Name Plate PQ-Node 3A PQ-Node 3BPQ-Node 3C RMS Volts 120 Volts 118 Volts 120 Volts 122 Volts to GND. RMScurrent 7.6 Amps 8.5 Amps 7.36 Amps 7.6 Amps Power factor 63% 70.5% 61%62% Load 15.79 ohms 13.88 Ohms 16.30 Ohms 16.05 Ohms Impedance Power574.6 Watts 707.12 Watts 538.75 Watts 574.86 Watts

Step 7: At this point, the multi-channel recorder has determined whatthe problem is and upgrades the overload warning to that of a motorfailure prediction caused by a shorted winding in the stator. Itidentifies the problematic motor phase based upon the PQ-Node inquestion and estimates the time before the motor reaches overloadstatus. It generates a work order to the maintenance staff or otherresponsible party detailing the above prognosis as well as identifyingthe location of the motor. The work order includes all pertinent motorinformation including the manufacturer, the motor model number, date itwas installed and an estimate in labor and materials to repair. Itcontinues to monitor the situation, upgrading the time to overload untilthe warning or alarm has been reset. The multi-channel recorder alsoquantifies the amount of energy lost during the period the motor hasbeen malfunctioning. A complete copy of the report and work order iskept in the report log that is created for each monitored load monitoredby this system.

This level of automated predictive analysis is completed almost as soonas it is initiated. The speed of an entry-level processor today runs thehandful of routines required for comparative analysis quite quickly. Inthis example, the work order detailing the problem and solution andestimated cost of the solution are generated before the RMS currentvalue ever reaches the alarm Region part of the predictive analysisregion. (See FIG. 15) This is long before the overload threshold valueis reached, which is the usual point when alarm notification is given.

Without the instant invention, the system shuts down the line when theautomatic overload protection disconnects the motor, which would be thefirst indication that there is a problem.

Not every electrical problem can be predicted because some failureshappen too quickly and do not afford the time to enact predictive orpreventative measures. However, most electrical failures are preventableand that is the purpose of this invention.

The above discussion has used a number of terms such as standarddeviation and compressed data blocks. The section below defines andexplains how these terms are derived and used in the system.

Standard Deviation:

Applying the concept of standard deviation to the study of electricalvariables is crucial to electrical fault prediction in the instantinvention. Sampling an electrical variable over time produces ahistorical reference that becomes the most important comparativecomponent to predictive analysis.

The first application of the standard deviation by the predictiveanalysis method is in the quantification of the volatility of the RMSvoltage and RMS current as it is measured by the PQ-Node and recorded ineach data block. As noted above, both the voltage and the current aremeasured over the full 12 cycles of a data block, producing 24 RMSvalues, each for the voltage and for the current. These values aresquared to remove the negative component on the 12 negative waveformsand then added together. This value is divided by the number of samples(in this case 24) and then the square root is taken to produce astandard deviation value for the voltage and a value for the current.

The standard deviation equation used to define the volatility of a datablock is defined as:

$\sigma = \sqrt{\frac{\left( {X - U} \right)^{2}}{N}}$

σ is the standard deviation

X is the RMS value of one of the 24 sampled current or voltagewaveforms.

U is the average RMS value for the current or the voltage waveforms.

N is the number of sampled waveforms, 24 in this case.

The most basic quantitative assessment of an electrical variable sampledover time is the derivation of the variable's average value and standarddeviation for that period of time. The standard deviation quantifies thestability, or volatility, of the baseline reference. This means that thestandard deviation of a variable's baseline reference, used as athreshold, provides an early indication of a change to the functionalcharacteristics of that variable with respect to its historical norm.

In addition, once calculated, the standard deviation is then divided bythe average RMS value, multiplied by 100 to produce the percent ofvariation used in the comparison of electrical values for determinationof data block compression. It is the percent of variation for thevoltage and for the current that is used in determining the relativevolatility of the data block. These two numbers, the voltage percentvariation and the current percent variation determine the compressioncycle for all data blocks. Unlike a threshold, which is a static value,the percent of variation is determined by the volatility of the 12cycles of voltage and current measured in each data block. This makesthe compression cycle functionally auto regulating, as the relativevolatilities of the data blocks are compared and if the volatilities arealike they are compressed and if not, they are separated. This in affectdivides time into sequential windows of volatility, which can be veryuseful in analysis. As noted above, a one hour span of time could haveas many as 18000 data blocks produced by a single PQ-Node or as littleas one, all depending on the degree of volatility and the change involatility occurring in that hour as measured by that PQ-Node.

Additionally, a standard deviation can be applied when analyzing a groupof like variables measured at different points on an electricaldistribution system at the same point in time. The measure of eachvariable of the group is compared to the average measure of the group asa whole, and this deviation is then measured and trended over time. Achange in the measured deviation of any one of the variables of thegroup with respect to the group as a whole can be significant.

The volatility of a variable is considered inversely proportional to thestability of that variable. If the volatility of a variable increases,then the stability of that variable decreases proportionately. As anexample, the percent of variation explained above is used to directlydefine and quantify the volatility of the voltage and current valuescontained in a data block. If a data block has an average value of 123.2Volts RMS and a 1.9% standard deviation, then the volatility of thevoltage for that data block is quantified at 1.9%. Conversely, thestability of the voltage for that data block is considered to be100%-1.9% or 98.1%.

Data Block Compression:

The reason for data block compression is to combine concurrent datablocks from the same PQ-Node that share the relative same electricalvalues and measured volatility and separate them from other data blocksof that PQ-Node that have greater or lesser degrees of volatility. Thisdivides the time periods of each PQ-Node into sequential windows ofvarying degrees of volatility. Each window of volatility defined by thestandard deviation measured for the voltage and current RMS valuesmeasured by that PQ-Node at the formation of the particular data blockinitiating that window.

A period of relative stability, where the electrical values measured bythe PQ-Node are relatively static, all falling within the standarddeviation set by the RMS voltage and current of the first data blockinitiating the compression cycle, may have a single data blockrepresenting the entire hour of monitoring. This might well occur if theload is turned off, of if the voltage is stable.

A period of considerable load shifting per hour will produce many datablocks, as the RMS current values will be fluctuating throughout thehour. This tells much about the load, giving specific real worldoperating parameters, linking the specific power factor and current THDvalues to a specific RMS current value.

PQ-Nodes that measure a transient voltage disturbance produce an RMSvoltage standard deviation that is too large to meet data blockcompression criteria, and thus will stand apart as a lone 200-miliseconddata block. Measured volatility in this way becomes a great tool forisolating moments in time and correlating them with specific locationson the electrical distribution system that measured the same volatileanomaly. This allows the analyst to look at the electrical distributionsystem as a whole, comparing the volatility of the voltage waveformsmeasured for specific time periods, and use this measure to identify theorigin and cause of voltage fluctuations as they occur.

Like throwing a stone into a pond, if we have PQ-Nodes scattered aroundthe edges of the pond, it doesn't matter whether we saw the locationwhere the stone enter the water or not. The PQ-Nodes along the bank willregister the ripple as it hits them, and comparing these ripples in timeand comparing the relative location of the PQ-Nodes to each other willallow us to determine where the stone entered the water.

Data Block Volatility:

The volatility of a data block is defined by the relative volatilitiesof the voltage and current values measured for the period of time thatthe data block encompasses, 200-miliseconds being the shortest timeperiod. Data block periods of longer than 200-miliseconds means that aperiod of relative stability has occurred. Because a data block'svolatility is described by the relative volatility of two differentvariables, the voltage volatility and the current volatility, adefinition and explanation of both follows.

Voltage Volatility is the average measured deviation of the RMS voltageover a 10/12-cycle period of time.

As explained above, the standard deviation of the RMS voltage is derivedfrom the 10/12-cycle period of measured RMS voltage values in a datablock. This standard of deviation measurement of the RMS voltage is thefundamental measure of volatility for the voltage variables of thatparticular data block. It is applied in percent value to the standarddeviation measure itself, as well as to the average RMS voltage and theDelta-VRMS values of the data block in question. This creates anenvelope of high and low threshold values for determining whether thedata block is itself a candidate for compression and whether or not thedata blocks that follow are suitable for compressing with it.

To use the VRMS standard deviation as a limit to data compression, thestandard deviation of the voltage, must itself have a limit imposed uponit so that this combining and separating of data blocks based upon theirrelative volatilities can be achieved. To do this, the standarddeviation of the first or initiating data block's V-RMS standarddeviation is applied to itself to create upper and lower boundarieswhich are used as limits to determine whether the standard deviation ofthe data blocks that follow it are suitable for compression.

As an example, if the V-RMS standard deviation of the initiating datablock is 1.9%, then the upper and lower boundary for the voltagestandard deviation values of the data blocks that follow it must fallwithin the range of 1.9%±1.9%. In this case, this produces an upperboundary of 1.94% and a lower boundary of 1.86%. This standard deviationboundary is set by the first data block and does not change for theduration of time represented by that data block.

The V-RMS standard deviation also imposes limits on the mean avg. V-RMSvalues for data block compression. Using the above example, if theinitiating data block records a mean avg. V-RMS value of 121.3 voltswith a standard deviation of 1.9% then the limits imposed upon the meanaverage. V-RMS values of the data blocks that follow are 121.3 Volts,±1.9%. This creates an upper boundary of 123.6 Volts and a lowerboundary of 118.9 volts. Thus, the mean average V-RMS values for thedata blocks that follow the initiating data block must fall within thisboundary to be eligible for compression. This limit is established bythe first or initiating data block and never changes for the duration ofthat compression cycle.

Similarly, the Delta V-RMS value of a data block also acts to limit todata block compression. If this value is too high, the data block cannotbe compressed and remains a single data block. For example, the averageRMS voltage is 121.3 volts and its standard deviation is 1.4%. A 1.4%variance in this case means a 3.4-volt wide operational envelopecentered on the RMS mean of 121.3 Volts. The Delta V-RMS of the datablock must be less than this 3.4-volt value for the data block to beeligible for compression. Once a data block becomes compressed, thevalue is dropped as a recorded value. The role of Delta V-RMS is tocreate and identify single data blocks. Consequently, compressed datablocks have no meaningful Delta V-RMS value.

The average RMS voltage and its standard deviation and the Delta-VRMSvalues of the data blocks that follow are compared to the range ofpermissible values for each variable mentioned above and if all of thevalues fall within the compression envelope, then the relativevolatility of the measured voltage values of the following data block isconsidered a suitable candidate for compression. The threshold valuescomprising the voltage compression envelope are defined by the firstdata block in the compression cycle and never change. This is summarizedin Table 2 below:

TABLE 2 Voltage Volatility Compression Envelope Example VoltagePermissible range of values for data block Variables Measuredcompression. Based upon the initial 1.4% from Value of 1^(st) Std. Dev.derived for the V-RMS over the data block data block 12-cycle data blockperiod. V-RMS Std. 1.4% 1.38% -to- 1.42% Dev. Avg. V-RMS 121.3 Volts119.6 Volts -to- 123.0 Volts, (3.4-Volt RMS Envelope) Delta V-RMS 2.8Volts Must be less than 3.4 Volts for compression to be allowed.

Current Volatility:

The volatility of the current is defined as the average measureddeviation of the RMS current over a 10/12-cycle period of time.

The concept of current volatility is the same as is the concept ofvoltage volatility explained above. The standard of deviationmeasurement of the RMS current is the fundamental measure of volatilityfor the current variables of that particular data block. As in the caseof voltage volatility, current volatility is applied in percent value tothe standard deviation measure itself, as well as to the average RMScurrent, the Delta-IRMS and the power factor values of the data block inquestion. This creates an envelope of high and low threshold values fordetermining whether the data blocks that follow are suitable forcompression. And like the voltage compression envelope discussed above,the threshold values comprising the current compression envelope aredefined by the first data block in the compression cycle, and neverchange.

The standard of deviation, the average RMS current, the Delta-IRMS andthe power factor values of the data blocks that follow are compared tothe range of permissible values for each variable mentioned above and ifall of the values fall within the compression envelope, then therelative volatility of the measured current values of the following datablock is considered a suitable candidate for compression. Table 3 is atable of current values as an example:

TABLE 3 current Volatility Compression Envelope Example CurrentPermissible range of values for data block Variables Measuredcompression. Based upon the initial 1.5% from Value of 1^(st) std. dev.derived for the I-RMS over the data block data block 12-cycle data blockperiod. I-RMS Std. 1.5% 1.48% -to- 1.52% Dev. Delta I-RMS 0.45 Amps 0.44Amps -to- 0.46 Amps Avg. I-RMS 12.4 Amps 12.21 Amps -to- 12.59 AmpsPower factor 82% 80.77% -to- 83.23%

Once it has been determined that a new data block is suitable forcompression, the associated voltage and current values of the new datablock are combined with their respective values in the initiating datablock to produce an average value for each of the data block's voltageand current elements described above. The data block has now become acompressed data block, whose electrical values, with a couple ofexceptions, have been averaged over time. The exceptions are the elapsedtime value, the electrical energy measurement in joules or watt seconds,and the maximum minimum peak voltage and peak current values. Theelapsed time and the electrical energy measurement values areaccumulative over time and are thus summed together. The maximum andminimum peak voltage and peak current values of the new data block arecompared with the compression data block, and if the maximum and/orminimum peak values are exceeded by the new data block those values arethen updated to reflect the newer greater and/or lesser values in thecompression data block.

Thus, the relative volatility of a data block is defined by these twofundamental measures: the standard deviation of the measured RMS voltageand the RMS current values for that 10/12-cycle period. For data blockcompression to commence, the associated values for the voltage andcurrent variables for the data blocks that follow must fall within thevoltage and current compression envelopes as explained above. Once oneor more of the voltage or current variables fall outside of thecompression envelope, the compression cycle ends and a new period ofmeasured volatility is defined and begun.

The dynamics of data block compression produces two basic kinds of datablocks. It produces data blocks that have undergone a compression cycleand those that have not. Data blocks that fail to compresses indicatethat one or more of the electrical values being measured haveexperienced a transient condition, which caused the measured values tofall outside the acceptable compression limits set by the data blockpreceding it or of the data block following it.

These uncompressed data blocks stand alone, apart from the rest,indicators of a transient condition of one kind or another. This processproduces a filtering effect; automatically segregating volatile datablocks whose values are in relative flux, from the rest of the datablocks, whose values were stable enough to undergo compression.

This effect facilitates the automation of the analysis method byindicating which blocks of time contain these uncompressed or singledata blocks. The CPU simply scans the 18,000 200-millisecond blocks oftime created every hour, searching for those blocks of time that hassingle data blocks. Single data blocks produced by different PQ-Nodes onthe network and created during the same block of time are related, andshare a common data set, which can be determined by simple maskingtechniques of the variables involved. This method provides an efficientway to automate the analysis method.

Additional Compression Limits

While the fundamental compression limit for a data block is the standarddeviation limit explained above, additional limits are introduced belowto further enhance and refine the focus of the masking techniques bydefining what kind of single data block it is. That is to say, did thedata block fail compression because of low voltage dips or high voltagetransients, or high current volatility, or some combination of multiplefactors? Below are described some additional limits that have beenimposed to further constrain data block compression.

Max Peak Voltage limit: this limit is set at 10-15% above the nominalpeak voltage. An RMS 120 volt nominal voltage has a peak value of 170volts. A 10% increase would represent a limit set at 187 volts. Any peakvoltage exceeding this amount would inhibit this data block fromcompressing. This limit is intended for transient capture. Manytransients are of such short duration that they may not appreciablyimpact the standard deviation limits set by the data block. This limitinsures that the event is captured so that high voltage transientanalysis may be performed. This data block would be flagged and includeadditional values, representing the transient's peak voltage and theduration of the event, in microseconds, that exceeded the prescribedlimit. This data is produced at the initial 153.6 kHz sample rate.

Min Peak Voltage limit: this limit is set at 10-15% below the nominalpeak voltage. An RMS 120 volt nominal voltage has a peak value of 170volts. A 10% decrease would represent a limit set at 153 volts. Any peakvoltage falling below this amount would inhibit this data block fromcompressing. This limit is intended for low voltage transient capture.Many transients are of such short duration that they may not appreciablyimpact the standard deviation limits set by the data block. This limitinsures that the event is captured so that low voltage transientanalysis may be performed. This data block would also be flagged andinclude additional values, representing the transient's voltage dip andthe duration of the event, in microseconds, that fell below theprescribed limit. This data is produced at the initial 153.6 kHz samplerate.

Max RMS Voltage limit: this limit is the 3^(rd) order of standarddeviation set by the data block. Any RMS voltage value for a single ½waveform that exceeds the 3^(rd) order standard deviation value for thatdata block would prevent it from being compressed. This would indicatean anomalous high voltage condition has occurred that demands furtheranalysis.

Min RMS Voltage limit: this limit is the 3^(rd) order of standarddeviation set by the data block. Any RMS voltage value for a single ½waveform that falls below the 3^(rd) order standard deviation value forthat data block would prevent it from being compressed. This wouldindicate an anomalous low voltage condition has occurred that demandsfurther analysis.

Voltage and Current Blocks Created During a Compression Cycle:

When a data block compression cycle begins, as explained above, theoutput of any particular PQ-Node changes. Instead of transmitting datablocks, the node periodically transmits voltage blocks and currentblocks. The purpose of these blocks of data is to supply the database inthe multi-channel recorder with waveform data for the purposes ofwaveform reconstruction and Fourier analysis. A brief description ofeach is given below:

Voltage Blocks: voltage blocks are blocks of data containing strings ofconcurrently sampled voltage values. Each voltage block contains 10/12voltage cycles or waveforms, each waveform containing 256 sampledvalues, for a total of 2560/3072 sampled values per voltage block. Eachcompression cycle causes at least one voltage block of 2560/3072 sampledvalues to be sent to the multi-channel recorder as representative of thevoltage waveform undergoing the present compression cycle. The number ofvoltage blocks sent for each compressed data block will depend upon thecompression length of the data block. These values are sent to thedatabase in the multi-channel recorder for further processing andFourier analysis. The Fourier analysis transforms the voltage signatureinto the frequency domain where harmonic analysis and Total HarmonicDistortion of the voltage signature can be determined.

Current blocks: current blocks are blocks of data containing strings ofconcurrently sampled current values. Each current block will contain10/12 current cycles or waveforms, each waveform containing 256 sampledvalues, for a total of 2560/3072 sampled values per current block. Eachcompression cycle will cause from one to as many as five current blocksof 2560/3072 sampled values each to be sent to the multi-channelrecorder as representative of the current waveform undergoing thecompression cycle. These values are sent to the database in themulti-channel recorder for further processing and Fourier analysis. TheFourier analysis transforms the current signature into the frequencydomain where conventional current signature analysis can be performed.The five sequential current blocks provide enough data points for thedesired frequency resolution required for load analysis.

Communication and Transmission of Data

Every PQ-Node has its own distinct dedicated 1-second window oftransmission time per minute, the initial handshaking that occursbetween the PQ-Node and the multi-channel recorder prior to thetransmission of data blocks is used to determine the accuracy of theinternal clocks of the PQ-Nodes with respect to the master clock in themulti-channel recorder. It is also initiated at a very specificprogrammed standardized time, in microseconds, at the beginning of each1-second window of transmission for all PQ-Nodes. The difference betweenwhen the multi-channel recorder received its initial query from thePQ-Node and when the MCR expected to receive the query (as measured byits own internal clock) is a measure of that PQ-Node's time clock errorplus that particular PQ-Node's signal propagation delay.

The propagation delay is determined by the multi-channel recorder duringthe initiation and start-up of the PQ-Node when it is first installed onthe network. This is accomplished by the multi-channel recorder bytransmitting an instruction routine to the PQ-Node that takes exactly100-microseconds to complete. This routine entails transmitting a simplereceive and transmit instruction to the PQ-Node, of known duration giventhe processor speed, plus a defined delay that totals 100-microsecondsexactly. The total time measured between the transmission of theinstruction by the multi-channel recorder to the PQ-Node and thesubsequent reception of the PQ-Nodes response to the multi-channelrecorder minus this 100-microsecond period indicates the totalpropagation delay between the multi-channel recorder and that PQ-Node.This measured delay, divided by 2 represents the propagation delaybetween the multi-channel recorder and that particular PQ-Node.

Once the time clock error is calculated by multi-channel recorder itthen sends a time clock adjustment instruction containing the erroradjustment in microseconds to the PQ-Node and has it adjust its internalclock by the amount of this measured error. The timing error isautomatically adjusted at least once every hour, which should keep thePQ-Nodes internal time clock consistent and reliably synched to themulti-channel recorder. (This is based upon transmission rates greaterthan 10 megabits/sec. (See, e.g., the IEEE 1588 Precision TimeProtocol).

Because data blocks are sent from the PQ-Nodes to the multi-channelrecorder at least once an hour, the clock drift between the PQ-Nodes onthe network and the multi-channel recorder is very small and correctedlong before it should become a factor.

FIG. 18a is graphic and textual explanation the first portion of aPQ-node data block transmission cycle. FIG. 19 is graphic explanationthe latter portion of a PQ-node data block transmission cycle. The inputregister in the multi-channel recorder is maintained as a copy of thehistory register in the PQ-Node. Any changes made to the historyregister are made to the input register. This reduces traffic betweenthe PQ-Nodes and the multi-channel recorder to just those elements whosevalues have changed since the last transmission.

Dynamic Resistance

Dynamic resistance is a term defined herein as: ΔV/ΔI

Where ΔV is defined as: (voltage @ I_(min)-voltage @I_(max)) and

ΔI is defined as: (I_(max)−I_(min))

This value is derived from the RMS voltage and current measurementstaken over the 12-cycle time period common to all data blocks.

A comparison of the dynamic resistance values taken between the PQ-Nodeat the service and the PQ-node at the load yields a measure as to howstable the electrical distribution system is in supplying the load. Thismeasure quantifies the electrical energy lost to the wiring between theservice and the monitored load being monitored. A sudden change in thismeasure can indicate weakening mechanical connections in thedistribution system.

The impedance, in ohms, of that part of an electrical distributionsystem supplying a monitored load is calculated by subtracting thedynamic resistance measured at the main service from the dynamicresistance at the monitored load in question. This value reflects themechanical integrity of all of the distribution components, i.e. thewiring, buss bars, circuit breakers, fuses and mechanical switches,connecting the monitored load to the electrical supply. The measuredimpedance of the electrical distribution system connecting the monitoredload to the electrical supply, multiplied by the square of the monitoredload's operating current reveals the amount of power lost to that partof the electrical distribution system connecting the load to the mainservice.

The voltage drop and the dynamic resistance, when taken together andcompared over time, are the best measures of the overall health of theelectrical distribution system as a whole. The voltage drop at amonitored load can be studied over a specific time period. From this,the average voltage drop and the standard deviation for the samples ofvoltage drop measured for that time period can be derived. These valuescan then be compared, phase-to-phase, for each load.

Moreover, one can average the voltage drop for all of the monitoredloads in that time period and derive the standard deviation. These canthen be compared to the individual voltage drops measured for each loadto the average of the total load to determine worst case in that timeperiod. These can then be trended over time.

Compare the dynamic resistance at a monitored load for the same specifictime period as above for the voltage drop. Derive the average resistanceand the standard deviation for the measured resistance samples for thattime period. Compare these values phase to phase for each load. Averagethe dynamic resistance for all of the monitored loads and derive thestandard deviation. Compare the individual resistances of each load tothe average of the total load to determine worst case. Those loads thatfell outside the standard deviation are a top priority. These values canbe trended over time; comparing each new measured dynamic resistancemeasurement to the historical trend and to the standard deviation, bothindividually and collectively.

Electrical Distribution Source Impedance Alarm Threshold:

The standard used in the design of electrical distribution systems isNFPA 70, The National Electric Code. It states that for an electricaldistribution system designed to provide reasonable efficiency, branchcircuit conductors should be sized to prevent a voltage drop of 3% atthe furthest outlet of power; and the voltage drop in both feeders andbranch circuits to the furthest outlet does not exceed 5%. (A branchcircuit supplying a monitored load is usually dedicated to thatparticular load and by definition is that part of the electricaldistribution system between the last over current protective device andthe load served.)

According to the above rule, for every 1000 watts of load that a branchcircuit supplies electrical power to, not more than 30 watts should beexpended in line losses conveying that electrical power to the load andnot more than 50 watts total between feeders and branch circuits. Thisstandard provides an industry accepted performance guide to use incomparative analysis of the calculated ideal source impedance, thebranch circuit impedance and the measured impedances recorded andtrended over time. Thus, an alarm threshold for the monitoring of anelectrical distribution system in this case (i.e., that of FIG. 1) isany impedance value causing a 3% or more voltage drop in the branchcircuit or a 5% accumulative voltage drop in the electrical distributionsystem supplying any monitored load monitored by this system ofdistributed power quality monitors. An alarm threshold is differentiatedfrom a warning threshold, which is more sensitive and is designed toproduce warnings of probable alarms should existing trends continue todeteriorate.

The alarm threshold provides an initial performance test of an existingcircuit. The initiation and startup of the PQ-Nodes for a monitored loadis performed by programming of the GUI discussed below. The idealimpedance values calculated indicate if the existing branch circuit andsubsequent electrical distribution system are adequate to supply theamount of power required by the addition of the critical load.

Electrical Distribution Source Impedance Warning Threshold:

Once the monitored load and its PQ-Nodes have been installed on thefacility's electrical distribution system, the source impedance andvoltage drop data collected by the PQ-Nodes and sent to themulti-channel recorder for analysis begin. Initial source impedancevalues are recorded and use for comparison of values sampled over time.A relative increase in source impedance of 10% compared to the initialvalue recorded for that PQ-Node elicits a source impedance warning,including an estimate of the additional power lost to the distributionsystem. This warning identifies the location in the electricaldistribution system where additional electrical maintenance should beemployed and records the accumulation of the additional energy that theweakening distribution system is responsible for until the maintenanceis performed and the source impedance values are brought down to lessthan 10%. The actual cost of the additional electrical energy wasted inthe electrical distribution system is quantified using the facility'sutility rate schedule. Further warnings can be added as follow ups tothe initial warning, based upon either response time to the warning andor the accumulated waste of energy reaching a certain user definedpredetermined amount.

Weekly, monthly, quarterly and yearly reports detailing the amount ofenergy lost to the facility's electrical distribution system can also begenerated. The report details the ideal minimum amount of energyexpended, and how much was actually expended. The difference in thesevalues indicates the amount of wasted energy borne by the facility inthe performance of its business. The cost to the business by the energywasted in the facility's electrical distribution system is quantified byusing the facility's utility rate schedule, as well as illustratingwhere in the facility's electrical distribution system the greatestwaste exists.

Analysis Techniques Provided by Data Block Compression:

Z-blocks: A Z-block is defined as a single data block in which theDelta-I exceeds the compression limits of the preceding data block andthe data block which follows it. This is the most common single datablock, and is created during periods of load shifting, when the currentmeasured in the circuit is either increasing or decreasing. It isreferred to here as a Z-block because it is at these times when thedynamic or source impedance is measured.

The source impedance is defined as: Delta V-RMS/Delta I-RMS. The DeltaV-RMS is defined as the difference in voltage between the last cyclerecorded in the data block and the first cycle recorded in the datablock and the Delta I-RMS is defined as the difference in currentbetween the last cycle recorded in the data block and the first cyclerecorded in the data block. The ratio of Delta-V-RMS/Delta-I-RMS revealshow stiff the source voltage and electrical distribution system is inresponse to fluctuations in current demand.

The source impedance is measured at each monitored load every time theload changes states, from off to on and from on to off or when the loadshifts from one current level to another. The change in voltage dividedby the change in current as seen by the load during these load shiftsreveals the impedance of the electrical distribution system supplyingthat load. This value would be averaged over time and a standarddeviation measure derived for it as well.

The analysis of Z-blocks is further expanded upon in the CircuitAnalysis Section below. The other single data blocks, in contrast, areindicative of transient or anomalous events that often portend seriousconditions arising in the electrical distribution system. These blocksare described below:

High Peak Voltage data block: this single data block is created when thepeak voltage limits, as explained above, have been exceeded. The purposeof this data block is to indicate the peak value of the high voltagetransient, the duration of the transient and the time that it occurred.The use of this single data block in transient analysis is explainedbelow in the Transient Analysis portion of the outline.

High Max RMS Voltage data block: this single data block is created whenthe max RMS voltage limit, has been exceeded. The purpose of this datablock is to indicate that a high RMS voltage event has occurred. Thissingle data block is used primarily in transient analysis and isexplained below in the transient analysis portion below.

Min Peak Voltage data block: this single data block is created when themin peak voltage limits, as explained above, have been exceeded. Thepurpose of this data block is to indicate the minimum value of the lowvoltage transient, the duration of the transient and the time that itoccurred. This single data block is used for both transient analysis andelectrical distribution analysis is explained below in the transientanalysis and the electrical distribution analysis portion below.

Low RMS voltage data block: this single data block is created when themin RMS voltage limit, as explained above, has been exceeded. Thepurpose of this data block is to indicate that a low RMS voltage eventhas occurred. This single data block is used primarily in electricaldistribution analysis, which is explained below in the electricaldistribution analysis portion below.

Transient Analysis Section:

As introduced earlier, transient voltage is probably the mostdestructive, costly and common power quality issue today. Voltagetransients cost companies and homeowners billions of dollars every yearin equipment damage, system downtime and lost production. Most peopleare familiar with the large external transients and the immediate damagethey can cause. This is because these large events are usuallyinstantaneous and the damage is visual. However, what many peopleoverlook is the serious damage the less noticeable and less intenseinternal transients are causing day after day. Internally generatedtransients are responsible for 80% of electrical and electronicequipment damage. These surges are the result of normal everydayequipment operations. Common culprits include; drives, motors, copiers,microwaves, printers, welders and lights. Many of these loads would bemonitored by the disclosed predictive fault analysis system.

It seems a natural progression then, that a predictive fault analysissystem employing the use of permanently installed distributedpower-monitoring sensors, would include the integration of transientvoltage surge suppression into the design of the distributed powersensors. This would have the beneficial effect of providing transientvoltage surge protection for any load monitored by the system as well asat the electrical distribution panels where the power monitors wouldalso be installed. This would greatly reduce the damaging effects thattransient voltages pose to a facility's electrical distributions systemand to the electrical loads supplied.

As transient analysis is one of the forms of analysis that is providedby the disclosed predictive fault analysis system, a brief discussion ofthe term transient, as it is used here, is warranted.

To explain, a “Transient” as introduced above, is the term for adisturbance that lasts less than one cycle. The IEEE Std. 1159-1995,Recommended Practice for Monitoring Electric Power Quality, is thestandard that is used here for the definition and classification oftransients. Transients are either impulsive or oscillatory in nature. Animpulsive transient is normally a single, very high impulse. Lightningis the most common cause of impulsive transients.

An oscillatory transient oscillates at the natural system frequency andnormally dies down within a cycle. These transients (sometimes calledswitching transients) occur when you turn off an inductive or capacitiveload, such as a motor or capacitor bank. An oscillatory transientresults because the load resists the change. This is similar to whathappens when you suddenly turn off a rapidly flowing faucet and hear ahammering noise in the pipes. The flowing water resists the change, andthe fluid equivalent of an oscillatory transient occurs.

TABLE 4 Summary of Types of Transient Conditions Transient CategoriesImpulsive Spectral Component Duration Millisecond 0.1 msec rise >1 msec(low frequency Microsecond 1 msec rise 50 nsec to 1 nsec (mediumfrequency) Nanosecond 5 nsec rise <50 nsec (high frequency OscillatoryLow frequency <5 kHz 0.3 msec to 50 msec Medium frequency 5 kHz to 500kHz 5 msec to 20 msec High frequency 0.5 MHz to 5 MHz 5 msec

Rise time and duration characterize impulsive transients, whilefrequency and duration characterize oscillatory transients. Bothimpulsive and oscillatory transients are subdivided into threecategories related to the frequencies. Low-frequency transients are thetypes most likely to occur in a power system. Medium-frequencytransients aren't as common, but they have much higher amplitudes. Onegenerally observes high-frequency transients only near the source of thedisturbance, and the response characteristics of the instrumenttransformers affect your ability to measure them.

The effect a transient has on a power system depends on the amplitude ofthe transient and its frequency. In the case of oscillatory transients,the frequency of the transient often causes problems—although theamplitude can cause problems as well. For example, oscillatorytransients can wreak havoc with Variable Speed Drives.

In the case of impulsive transients, the amplitude of the transientoften causes problems. The damage caused by a transient can beimmediate, as is sometimes the case with a lightning strike, or gradual,as in a response to a series of low-amplitude transients. Theselow-amplitude transients slowly degrade insulation until a short circuiteventually occurs. Instead of immediate damage by one high-amplitudetransient, low amplitude transients can occur repetitively thousands oftimes a day. These surges are damaging and can be as high as 6000 volts!Internal transient damage is rarely noticed because these events aretypically not instantaneously damaging nor is the damage visual. Thecumulative damage isn't noticed until the system is upset or fails. Theinstallation of a distributed system of PQ-Nodes with integratedtransient voltage surge suppression would shed many of these transientsto ground, there by isolating the monitored loads from their harmfuleffects.

From an electrical distribution analysis point of view, the disclosedpredictive analysis system uses the PQ-Nodes, distributed throughout thefacility's distributions system, to locate the origin and classify theelectrical transients recorded. By comparing the measured values of asingle transient, recorded by several different PQ-Nodes distributed atdifferent points on the electrical distribution system, themulti-channel recorder can determine the origin of the transient as wellas classifying the type of transient condition. The periodic energyreports produced by the disclosed predictive analysis system's softwareinclude a detailed account of the number of transients recorded by thedistributed PQ-Node monitoring system as well as their origins andclassifications. Facility loads that produce harmful transients areidentified.

From a load analysis point of view the transient signature created whena monitored load is initially turned on or off can be measured andanalyzed it to determine the load's inductive or capacitive reactivecomponent, as seen by the electrical distribution system, which canreveal the symptoms that predict operational failure because fundamentalchanges in the reactive component of a load's impedance indicate the waythat the load converts electrical energy to work has changed; and thisusually means a dynamic shift has occurred in the operational parametersthat define the monitored load. In this way, studying the transientsproduced by a monitored load as it changes states provides the disclosedpredictive analysis system another measure with which to gage the healthof a monitored load.

The disclosed predictive analysis system uses the PQ-Nodes, distributedthroughout the facility's distributions system, to locate the origin andclassify the electrical transients recorded. By comparing the measuredvalues of high peak voltage high max RMS voltage, and the min peakvoltage, a single transient, recorded by several different PQ-Nodesdistributed at different points on the electrical distribution system,the multi-channel recorder can determine the origin of the transient aswell as classifying the type of transient condition. The classificationof the transient is determined with respect to the IEEE Std. 1159-1995,Recommended Practice for Monitoring Electric Power Quality, asintroduced above or other applicable industry accepted standards. Theperiodic energy reports produced by the disclosed predictive analysissystem's software include a detailed account of the number of transientsrecorded by the distributed PQ-Node monitoring system as well as theirorigins and classifications. Facility loads that produce harmfultransients are identified.

Electrical Distribution or Circuit Analysis Section:

Dynamic Impedance: as noted above, this is a comparison of the dynamicresistance values taken between the PQ-Node at the service and thePQ-node at the load and yields a measure as to how stable the electricaldistribution system is supplying the load. This measure quantifies theelectrical energy lost to the wiring between the service and thecritical load being monitored. A sudden change in this measure canindicate a weakening mechanical connection in the distribution system.

The impedance of that part of an electrical distribution systemsupplying a critical load is calculated by measuring the dynamicresistance at the critical load in question, as defined above, andsubtracting from this number the dynamic resistance measured at the mainservice. This reveals the impedance in ohms of the distribution systembetween the main service and the critical load in question. This valuereflects the mechanical integrity of all of the distribution components,i.e. the wiring, buss bars, circuit breakers, fuses and mechanicalswitches, connecting the critical load to the electrical supply.

The measured impedance of the electrical distribution system connectingthe critical load to the electrical supply, multiplied by the square ofthe critical load's operating current reveals the amount of power lostto that part of the electrical distribution system connecting the loadto the main service.

The following section provides a series of examples for how this systemcan be used. FIG. 19 is a representation of a Graphical User Interface(GUI) system interface for representation of Electrical DistributionSystem of FIG. 1. The software is located in multi-channel recorder andaccessed directly or via system server.

FIG. 20 is a representation of an example of predictive fault analysissystem's software of graphical user interface for representation ofcritical load data. This software is located in multi-channel recorderand accessed directly or via system server. Analysis Fundamentals:

A facility's maintenance department is generally tasked with the properupkeep, maintenance and repair of the electrical distribution system, aswell as the troubleshooting and repairing the various loads that areintegral with the facility's business or reason for being. Whentroubleshooting electrical problems, determining whether a fault (or apredication of a fault) was caused by the electrical distribution systemor the load is the first question asked. For this reason, the analysismethod splits the analysis into these two parts, electrical distributionanalysis and load analysis.

Analysis of the Electrical Distribution System:

Referring again to FIG. 1, the electrical distribution system of afacility conveys electrical energy from the point where the servingutility delivers it, usually the main service of the facility, to theelectrical load. It is composed of wiring, circuit breaker panels,disconnects, transformers and other elements that are mechanicallybonded together to form a low resistance path to serve the electricalappliances or loads that converts the electricity to useful work.

As discussed above, the health of an electrical distribution system canbe determined by monitoring and measuring the impedance of the branchcircuits delivering power to the various loads and recording thesevalues and comparing them over time. A sudden change or increase in theimpedance of a particular branch circuit signifies an increase in theenergy consumed by the branch circuit in the form of heat. Such heatcreated in the electrical branch circuit can cause serious problems ifnot caught in time for it is a major cause of building fires.

Electrical Distribution Analysis Example

As noted above, when a PQ-Node is installed on an electricaldistribution system, its relative location with respect to the monitoredload and all other PQ-Nodes pertinent to the protection of that criticalload is defined in the analysis software via a graphical user interface.See, e.g., FIG. 19. A PQ-Node installed at the monitored load is locatedat the end of a branch circuit and monitors only the current drawn bythat monitored load. A PQ-Node that is located at a breaker panel orfeeder monitors current that is drawn by multiple loads and thereforehas a different perspective of the electrical distribution system from acomparative analysis point of view than the PQ-Node located at themonitored load. This is an important distinction between these twoPQ-Nodes. While the hardware is the same, their relative placement on anelectrical distribution system means that the data collected from thetwo will, in some cases, be compared differently. The comparativedifferences between the various PQ-Nodes are defined in the software atthe multi-channel recorder and are integral to the creation anddefinition of the various comparative algorithms that constitute theautomated analysis method. This difference is defined in software whenthe symbolic PQ-Nodes are placed onto a graphical user interfacerepresenting the electrical distribution system. This is explainedbelow.

The graphical user interface, or GUI, shown in FIG. 19, resembles anelectrical power schematic showing the monitored load, the main serviceand that part of the electrical distribution system connecting themonitored load to the main service. The GUI includes basic electricaldistribution symbols that represent the actually physical hardware thatconstitutes the real electrical distribution system. These electricaldistribution symbols can be selected by the installer, via drop downmenus from a tool bar that is located at the top of the operatorinterface screen (not shown). The appropriate symbols are selected,dragged and dropped into place to build a graphical representation ofthe actual electrical distribution system connecting the monitored loadto the electrical supply. The PQ-Node symbols are selected, dragged anddropped unto the GUI's representative electrical distribution system inthe same relative place that they are installed physically. EachPQ-Node's ID and the multi-channel recorder's ID that the PQ-Nodecontacts is identified by typing the ID into a text block that appearsin every PQ-Node and multi-channel recorder symbol. The phase andvoltage magnitude along with the circuit breakers, electrical switches,wire size and estimated length of the branch circuit or feederconductors associated with each PQ-Node is similarly identified andinputted into the analysis software.

Each symbol selected in the building of the GUI's representativeelectrical distribution system has a corresponding programmedtemperature adjusted impedance value. These values are derived frommanufacture's operating and performance data, National Bureau ofStandards Handbook 100, and in-house micro-ohm testing performed duringPQ-Node installation and startup. As the symbols are selected andconnected together, an estimated impedance value for that part of theelectrical distribution system is calculated. The programmed impedancevalues for the circuit breakers, electrical switches, wire size andestimated length of the branch circuit or feeder conductor informationassociated with each PQ-Node are summed together to create a totaltemperature-compensated impedance value for that part of the electricaldistribution system monitored by that PQ-Node. This estimated impedancevalue is adjusted continuously and automatically, based upon thetemperature fluctuations recorded at the PQ-Nodes in the data blocksthat are sent to the multi-channel recorder. The purpose for temperaturecompensation of the “ideal” impedance values is to compensate for theeffect that fluctuating ambient temperatures have on actual impedancevalues. If the estimated “ideal” impedance value is to be used as abaseline reference for comparison purposes to actual values measured atthe PQ-Nodes, then this it must fluctuate as ambient temperaturefluctuates.

The programming for this is done either at the multi-channel recorderfor the PQ-Node in question, via a laptop or similar computerperipheral, or at the system server.

Once all the PQ-Nodes related to the protection of a monitored load havebeen installed on the electrical distribution system, and their relevantinformation has been inputted into software via the graphical userinterface described above, monitoring of the electrical distributionsystem is begun by the multi-channel recorder. As discussed above, themulti-channel recorder initiates contact with each of the PQ-Nodes thathave been identified in the software; it sets the internal time clock ofthe PQ-Node and establishes its unique transmission cycle. Onceinitialization is completed by the multi-channel recorder, the PQ-Nodebegins to measure the voltage and current values in 200-milisecondblocks of time as explained above. The PQ-Nodes transmit the data to themulti-channel recorder at varying rates per hour depending upon thevolatility of the data collected, via the data log transmission mode orimmediately if requested by the multi-channel recorder via the alarmtransmission mode. The multi-channel recorder compares the electricalvalues from different PQ-Nodes whose relative relationships have beenestablished and defined in the analysis software via the initializationand startup procedure explained above.

As an example, referring to FIG. 20, the dynamic resistances forPQ-Nodes 1A, 2A and 3A are calculated. This calculation is done bymeasuring the voltage change at the various PQ-Nodes distributed inseries along the distribution system in response to the change in thecurrent drawn by the monitored load.

The 200-milisecond data block created at PQ-Node 3A contains the recordof a sudden shift in current and subsequent change in voltage asmeasured at that PQ-Node in the same data block that is poled fromPQ-Nodes 1A and 2A by the multi-channel recorder for comparison andanalysis purposes. This insures that the sudden current increase andsubsequent voltage drop measured at PQ-Node 3A is the same currentincrease and cause for the relative voltage drops measured at PQ-Nodes1A and 2A respectively. It is this synching of electrical measurementsrecorded at the same time and taken at different locations on theelectrical distribution system that makes the dynamic resistancemeasurement a useful tool for the instant analysis method.

This value is used to determine the amount of energy lost to the phase-Aportion of the electrical distribution system in the form of heat. Asnoted above, this is the source impedance, indicated with the letter Z.The source impedance at PQ-Node 3A, located at the monitored loadrepresents the total source impedance, as seen by the load, for thephase-A part of the distribution system. This value is compared to theideal source impedance value as calculated by the software, explainedabove. The difference represents the amount of energy wasted by theelectrical distribution system and, which can be potentially saved withproper maintenance. This value is recorded and charted over time and isa key addition to a facility's preventative maintenance schedule.

The impedance of the branch circuit supplying the load is equal to thesource impedance as measured at PQ-Node 3A minus the source impedancemeasured at PQ-Node 2A, located at the end of the feeder conductor, inthe branch circuit panel, as indicated in FIG. 20. The impedance valuefor the feeder conductors and its associated hardware is derived bysubtracting the source impedance measured at PQ-Node 1A, located at themain service, from the source impedance measured by PQ-Node 2A. Theimpedance values derived for the branch circuit, feeder and main servicefor the electrical distribution system supplying the monitored load inquestion are the fundamental values of concern with regards to assessingthe health and reliability of that part of the electrical distributionsystem. If any part of the electrical distribution supplying themonitored load fails, then the monitored load fails.

These three PQ-Nodes, 1A, 2A and 3A monitor the phase-A portion of theelectrical distribution supplying the monitored load. The phase-B andthe phase-C portions of the electrical distribution system are monitoredby the PQ-Nodes 1B, 2B and 3B and PQ-Nodes 1C, 2C and 3C respectively.An example of using the measured source impedance from the threePQ-Nodes that monitor the monitored load in FIG. 20 and calculating thetotal power lost to the electrical distribution system supplying themonitored load is explained in table 3 below. The multi-channel recorderpolls the relevant data, as displayed in table 4 below, from the samedata block produced by each of the PQ-Nodes 3A, 3B and 3C. It squaresthe change in current, (Delta I), multiplies it by the impedance, (DeltaZ), and the power factor measured for each PQ-Node to produce thewattage for that phase of the distribution system that the PQ-Nodemonitors. The multi-channel recorder then sums the wattages from each ofthe PQ-Nodes to reveal the total power lost to the electricaldistribution system with respect to the operation of the monitored load.

TABLE 5 Using Source Impedance to Calculate Power lost to DistributionSystem Power Power loss Delta I Delta V Delta Z factor in Wiring PQ-Node3A 20.79 Amps 2.89 Volts 0.1390 Ohms 0.87 52.27 watts PQ-Node 3B 20.90Amps 2.95 Volts 0.1411 Ohms 0.87 53.64 watts PQ-Node 3C 20.86 Amps 2.92Volts 0.1400 Ohms 0.87 52.97 watts Avg. Value 20.85 Amps 2.92 Volts0.1400 Ohms 0.87 52.97 watts

Total Power lost to Electrical Distribution for this load: 158.9 watts

Because the length and makeup of the different phase conductors for thebranch circuit and the feeder conductors supplying the monitored loadare nearly identical, it follows that the voltage dropped and thesubsequent impedance measured for them should be the same. Thus, theimpedance measured at PQ-Nodes 3A, 3B and 3C, for the branch circuitconductors, should be the same and the impedance values measured atPQ-Node 2A, 2B and 2C, for the feeder conductors, should as well be thesame.

If the associated wire lengths of the three phase conductors for thebranch circuit conductors and for the feeder conductors are the same,then the deviation or difference in impedance values measured betweenthe phases is most likely caused by a loosening mechanical connection.Detecting a loosening or deteriorating mechanical connection is thefundamental purpose and aim of the electrical distribution analysismethod here.

A loosening or deteriorating mechanical connection can evolve into aseries arcing fault, which is a partial or total failure in one of theconductive paths in series with the load. This failure is characterizedby a completely opened conductor path (which is indicated by an infiniteresistance) or by resistance alternating between infinite to highresistance and back again. It can take the form of an intermittentconnection, such as loose wire terminals, poor splices, or poorcontacts. All of these are classed as high resistance faults. It cantake the form of a glowing contact is a kind of series arcing fault thatis the result of abnormally high resistance in the wiring or terminalconnection resulting in the reduction of current flow and the excessiveheat at the fault. This high resistance fault can become extremely hotwith temperatures exceeding 600 degrees Fahrenheit. Obviously, detectingthis kind of fault is of great importance since it can lead to theignition of building materials and is responsible for many electricalfires every year. These kinds of high resistance faults becomecatastrophic over time because they are allowed to. Most of them arehidden and go unnoticed until they become catastrophic. Thus, detectionof these high resistance faults and the subsequent production of areport warning a facility's maintenance department as to the nature andlocation of the fault is the fundamental aim of this part of theautomated analysis system.

By continuous monitoring of impedance values and voltage drops measuredby the various PQ-Nodes distributed throughout the electrical system,and comparing these values to the historical trends that have beendeveloped over time, any deviation from the norm is identified early on.These faults are noticed almost immediately, as their telltale highresistance condition stands out against the backdrop of the idealimpedance baseline created when the PQ-Nodes were first initiated andthe historical trends that have been developed over time. Locating thefault is as simple as identifying the PQ-Node that produced the datablock with the high impedance values.

The use of standard deviation as a measure of the volatility of thecurrent and the voltage described above is employed here as well in themonitoring for intermittent high resistive faults as described above. Asan example, the voltage difference between PQ-Node 3A and PQ-Node 2A inFIG. 13 is the voltage drop between the two and is directly proportionalto the branch circuit impedance in the wiring connecting the two. Anerratic, intermittent voltage drop is indicative of erratic andintermittent branch circuit impedance. If a comparison of the standarddeviation for the voltages recorded at both PQ-Nodes revealed a higherstandard deviation for the voltage at PQ-Node 3A than at PQ-Node 2A,then the erratic voltage drop between the two PQ-Nodes in question ismost likely caused by an intermittent high resistance fault located inthe wiring connecting the two. Troubleshooting and determining the causeof an intermittent fault condition can be one of the toughest partssystem analyses. The use of standard deviation in the quantification ofvoltage volatility and comparing this measure to various points alongthe electrical distribution system in this case provides an earlywarning of an intermittent high resistance fault condition.

The present disclosure should not be construed in any limited senseother than that limited by the scope of the claims having regard to theteachings herein and the prior art being apparent with the preferredform of the invention disclosed herein and which reveals details ofstructure of a preferred form necessary for a better understanding ofthe invention and may be subject to change by skilled persons within thescope of the invention without departing from the concept thereof.

What is claimed is:
 1. A system for collecting blocks of data used inpredicting faults in an electrical system having a source line, branchcircuits, and a plurality of loads comprising: a) a plurality of powerquality nodes, said plurality of power quality nodes being disbursedabout said electrical system on said source line, said branch circuitsand each of said plurality of loads, each of said power quality nodesconfigured for sensing voltage and current, a microprocessor, anon-volatile memory, an alarm transmitter/receiver and a data loggertransmitter/receiver; b) at least one multi-channel recorder, incommunication with each of said plurality of power quality nodes; c) atleast one network server in communication with said multi-channelrecorder; and d) a base computer station in operative communication withsaid multi-channel recorder and said network server; e) wherein thenon-volatile memory on each of said plurality of power quality nodesstores a plurality of data blocks for transmission to said multi-channelrecorder, and further wherein each of said data blocks contains aplurality of readings of said electrical system and further wherein eachof said data blocks has a time length; wherein the readings stored ineach stored data block contains data having a plurality of recordedparameters; and wherein the plurality of recorded parameters includes:a) a power quality node ID; b) Maximum Peak voltage; c) Minimum Peakvoltage; d) Maximum Peak voltage of the data block; e) Minimum Peakvoltage of the data block; f) Average Root Mean Squared (RMS) voltage;g) Maximum Peak current; h) Minimum Peak current; i) Average RMScurrent; j) Standard deviation of the RMS current; and k) a factor DeltaV/Delta I.
 2. The fault predicting system of claim 1 wherein theplurality of recorded parameters further includes: a) watts; b) joules;c) power factor; and d) an accumulated time stamp.
 3. The faultpredicting system of claim 1 wherein data-blocks having a plurality ofrecorded parameters that are within a specified standard deviation forsaid recorded parameters are compressed.
 4. A system for collecting dataused in predicting faults in an electrical system, the systemcomprising: a plurality of power quality nodes, each configured forgenerating a series of electrical value sets, each electrical value setbased on samples of a series of voltage cycles and a series of currentcycles from a conductor of the electrical system to which the powerquality node is coupled, the samples made by the power quality nodeduring one of a series of time blocks, each of the power quality nodesconfigured for storing a series of data blocks, wherein each of the datablocks contains a data block value set based on one or more of theelectrical value sets; wherein each of the power quality nodes isconfigured for generating a set of compression criteria for each datablock based on the data block value set for that data block; and whereineach of the power quality nodes is configured for generating the seriesof data blocks by compressing the electrical value set of a present timeblock of the series of time blocks into a data block value set of apreceding data block of the series of data blocks if the electricalvalue set of the present time block meets one or more compressioncriteria of the set of compression criteria of the preceding data block,else creating a new data block of the series of data blocks with theelectrical value set of the present time block as a data block value setof the new data block.
 5. The system of claim 4, wherein the data blockvalue set of the preceding data block is based an electrical value setfor a preceding time block, the preceding time block one of the seriesof time blocks preceding the present time block.
 6. The system of claim4, wherein the time blocks are 200 milliseconds in duration.
 7. Thesystem of claim 4, wherein the time blocks are of equal duration in amultiple of 200 milliseconds.
 8. The system of claim 4, wherein eachelectrical value set includes one or more of: a time block maximum peakvoltage; a time block minimum peak voltage; a time block average RMSvoltage; a time block RMS voltage standard deviation; a time blockmaximum RMS voltage; a time block delta RMS voltage; a time blockmaximum peak current; a time block minimum peak current; a time blockaverage RMS current; a time block RMS current standard deviation; a timeblock maximum RMS current; a time block delta RMS current; a time blockpower factor; and a time block source impedance.
 9. The system of claim8, wherein each voltage cycle comprises two voltage half cycles; whereineach current cycle comprises two current half cycles; wherein the timeblock maximum peak voltage is a maximum peak voltage of the conductorduring the time block; wherein the time block minimum peak voltage is aminimum peak voltage of the conductor during the time block; wherein thetime block average RMS voltage is an average of an RMS (Root MeanSquared) voltage of each of the voltage half cycles of the conductorover the time block; wherein the time block RMS voltage standarddeviation is a standard deviation of an RMS voltage of each of thevoltage half cycles of the conductor during the time block; wherein thetime block maximum RMS voltage is a maximum RMS voltage of any halfvoltage cycle of the conductor during the time block; wherein the timeblock delta RMS voltage is a value that is a difference between an RMSvoltage of a last voltage cycle of the conductor in the time block andan RMS voltage of first voltage cycle of the conductor in the timeblock; wherein the time block maximum peak current is a maximum peakcurrent of the conductor during the time block; wherein the time blockminimum peak current is a minimum peak current of the conductor duringthe time block; wherein the time block average RMS current is an averageof an RMS current of each of the current half cycles of the conductorduring the time block; wherein the time block RMS current standarddeviation is a standard deviation of the RMS current of each of thecurrent half cycles of the conductor during the time block; wherein thetime block maximum RMS current is a maximum RMS current of any halfcycle of the conductor during the time block; wherein the time blockdelta RMS current is a value that is a difference between an RMS currentof a last current cycle of the conductor in the time block and an RMScurrent of a first current cycle of the conductor in the time block;wherein the time block power factor is an angle between the voltagecycles and the current cycles of the conductor during the time block;and wherein the time block source impedance is a quotient of the timeblock delta RMS voltage divided by the time block delta RMS current. 10.The system of claim 8, wherein each data block value set includes one ormore of: a data block maximum peak voltage; a data block minimum peakvoltage; a data block average RMS voltage; a data block RMS voltagestandard deviation; a data block maximum RMS voltage; a data block deltaRMS voltage; a data block maximum peak current; a data block minimumpeak current; a data block average RMS current; a data block RMS currentstandard deviation; a data block maximum RMS current; a data block deltaRMS current; a data block power factor; and a data block sourceimpedance.
 11. The system of claim 10, wherein each voltage cyclecomprises two voltage half cycles; wherein each current cycle comprisestwo current half cycles; wherein the data block maximum peak voltage isa maximum peak voltage of the conductor during the data block; whereinthe data block minimum peak voltage is a minimum peak voltage of theconductor during the data block; wherein the data block average RMSvoltage is an average of an RMS (Root Mean Squared) voltage of each ofthe voltage half cycles of the conductor over the data block; whereinthe data block RMS voltage standard deviation is a standard deviation ofan RMS voltage of each of the voltage half cycles of the conductorduring a first uncompressed electrical value set of the data block;wherein the data block maximum RMS voltage is a maximum RMS voltage ofany half voltage cycle of the conductor during the data block; whereinthe data block delta RMS voltage is a value that is a difference betweenan RMS voltage of a last voltage cycle of the conductor in the datablock and an RMS voltage of first voltage cycle of the conductor in thedata block; wherein the data block maximum peak current is a maximumpeak current of the conductor during the data block; wherein the datablock minimum peak current is a minimum peak current of the conductorduring the data block; wherein the data block average RMS current is anaverage of an RMS current of each of the current half cycles of theconductor during the data block; wherein the data block RMS currentstandard deviation is a standard deviation of the RMS current of each ofthe current half cycles of the conductor during a first uncompressedelectrical value set of the data block; wherein the data block maximumRMS current is a maximum RMS current of any current half cycle of theconductor during the data block; wherein the data block delta RMScurrent is a value that is a difference between an RMS current of a lastcurrent cycle of the conductor in the data block and an RMS current of afirst current cycle of the conductor in the data block; wherein the datablock power factor is an angle between the voltage cycles and thecurrent cycles of the conductor during the data block; and wherein thedata block source impedance is a quotient of the data block delta RMSvoltage divided by the data block delta RMS current.
 12. The system ofclaim 10, wherein the set of compression criteria includes one or moreof: the time block RMS voltage standard deviation of the present timeblock not deviating from the data block RMS voltage standard deviationof the preceding data block by more than a percent variation form of thedata block RMS voltage standard deviation of the preceding data block;the time block average RMS voltage of the present time block notdeviating from the data block average RMS voltage of the preceding datablock by more than the percent variation form of the data block RMSvoltage standard deviation of the preceding data block; the time blockdelta RMS voltage of the present time block less than the percentvariation form of the data block RMS voltage standard deviation of thepreceding data block; the time block maximum RMS voltage of the presenttime block not exceeding the average RMS voltage of the preceding datablock by more than three standard deviations of the data block RMSvoltage standard deviations of the preceding data block; the time blockRMS current standard deviation of the present time block not deviatingfrom the data block RMS current standard deviation of the preceding datablock by more than a percent variation form of the data block RMScurrent standard deviation of the preceding data block; the time blockaverage RMS current of the present time block not deviating from thedata block average RMS current of the preceding data block by more thanthe percent variation form of the data block RMS current standarddeviation of the preceding data block; the time block delta RMS currentof the present time block less than the percent variation form of thedata block RMS current standard deviation of the preceding data block;the time block maximum RMS current of the present time block notexceeding the data block average RMS current of the preceding data blockby more than three standard deviations of the data block RMS currentstandard deviations of the preceding data block; the time block maximumpeak voltage of the present time block not exceeding a maximum peakvoltage threshold set above the data block maximum peak voltage of thepreceding data block; the time block minimum peak voltage of the presenttime block not falling below a minimum peak voltage threshold set belowthe data block minimum peak voltage of the preceding data block; thetime block maximum peak current of the present time block not exceedinga maximum peak current threshold set above the data block maximum peakcurrent of the preceding data block; and the time block minimum peakcurrent of the present time block not falling below a minimum peakcurrent threshold set below the data block minimum peak current of thepreceding data block.
 13. The system of claim 10, wherein the set ofcompression criteria includes all of: the time block RMS voltagestandard deviation of the present time block not deviating from the datablock RMS voltage standard deviation of the preceding data block by morethan a percent variation form of the data block RMS voltage standarddeviation of the preceding data block; the time block average RMSvoltage of the present time block not deviating from the data blockaverage RMS voltage of the preceding data block by more than the percentvariation form of the data block RMS voltage standard deviation of thepreceding data block; the time block delta RMS voltage of the presenttime block less than the percent variation form of the data block RMSvoltage standard deviation of the preceding data block; the time blockmaximum RMS voltage of the present time block not exceeding the averageRMS voltage of the preceding data block by more than three standarddeviations of the data block RMS voltage standard deviations of thepreceding data block; the time block RMS current standard deviation ofthe present time block not deviating from the data block RMS currentstandard deviation of the preceding data block by more than a percentvariation form of the data block RMS current standard deviation of thepreceding data block; the time block average RMS current of the presenttime block not deviating from the data block average RMS current of thepreceding data block by more than the percent variation form of the datablock RMS current standard deviation of the preceding data block; thetime block delta RMS current of the present time block less than thepercent variation form of the data block RMS current standard deviationof the preceding data block; the time block maximum RMS current of thepresent time block not exceeding the data block average RMS current ofthe preceding data block by more than three standard deviations of thedata block RMS current standard deviations of the preceding data block;the time block maximum peak voltage of the present time block notexceeding a maximum peak voltage threshold set above the data blockmaximum peak voltage of the preceding data block; the time block minimumpeak voltage of the present time block not falling below a minimum peakvoltage threshold set below the data block minimum peak voltage of thepreceding data block; the time block maximum peak current of the presenttime block not exceeding a maximum peak current threshold set above thedata block maximum peak current of the preceding data block; and thetime block minimum peak current of the present time block not fallingbelow a minimum peak current threshold set below the data block minimumpeak current of the preceding data block.
 14. The system of claim 4,further comprising: a multi-channel recorder configured for generating,for each of the plurality of power quality nodes, a load profile, theload profile based on the data block value sets generated by the powerquality node during a series of load cycles, each load cycle covering aperiod of time from when a load is started to when the load is stopped,the load coupled to the conductor to which the power quality node iscoupled; wherein the multi-channel recorder is configured forgenerating, for the each of the plurality of power quality nodes, one ormore notification thresholds based the load profile of the load coupledto the power quality node; and wherein each of the plurality of powerquality nodes is configured for generating notifications based oncomparing the one or more notification thresholds with the electricalvalue set of the present time block.
 15. The system of claim 14, whereingenerating a load profile comprises: generating, for each of the seriesof load cycles, a set of load cycle operational characteristics and aset of load cycle operational characteristic standard deviations;generating a set of load profile operational characteristics based onaveraging each of the load cycle operational characteristics over eachof the series load cycles; and generating a set of load profileoperational characteristic standard deviations based on a standarddeviation of each of the load cycle operational characteristics overeach of the series load cycles.
 16. The system of claim 15, wherein theset of load cycle operational characteristics includes one or more of: aload cycle average RMS current; a load cycle average RMS currentstandard deviation; a load cycle average power factor; a load cycleaverage power factor standard deviation; a load cycle average currentTHD (Total Harmonic Distortion); a load cycle average current THDstandard deviation; a load cycle data block RMS current standarddeviation; a standard deviation of the load cycle data block RMS currentstandard deviation; and a load cycle stability index.
 17. The system ofclaim 16, wherein generating a set of load cycle operationalcharacteristics for each of the series of load cycles comprises:generating the load cycle average RMS current by multiplying a datablock average RMS current of each of the compressed data blocks in theload cycle times a data block time length of the compressed data block,resulting in a series of products, summing the products to a sum, thendividing the sum by a number of compressed data blocks in the loadcycle.
 18. The system of claim 16, wherein generating a set of loadcycle operational characteristics for each of the series of load cyclescomprises: generating the load cycle average current THD based on one ormore current blocks received from the power quality node, each currentblock comprising a plurality of samples of the current of the conductorof the power quality node.
 19. The system of claim 16, whereingenerating the set of load profile operational characteristicscomprises: generating the load cycle stability index by subtracting theload cycle average RMS current from a stability metric, wherein thestability metric is calculated by taking a number of time blocksrepresented by the compressed data blocks in the load cycle and dividingby a total number of time blocks in the load cycle.
 20. The system ofclaim 16, wherein the set of load profile operational characteristicsincludes one or more of: a load profile average RMS current; a loadprofile average RMS current standard deviation; a load profile averagepower factor; a load profile average power factor standard deviation; aload profile average current THD; a load profile average current THDstandard deviation; a load profile data block RMS current standarddeviation; a standard deviation of the load profile data block RMScurrent standard deviation; and a load profile stability index.
 21. Thesystem of claim 15, wherein generating, for the each of the plurality ofpower quality nodes, one or more notification thresholds comprises:setting each notification threshold to one of the load profileoperational characteristics plus one or more multiples of acorresponding one of the load profile operational characteristicstandard deviations.
 22. The system of claim 4, wherein the powerquality nodes are configured for generating a plurality of dynamicresistance values, generating each dynamic resistance value for one ofthe power quality nodes for one of the time blocks by dividing a delta Vvalue by a delta I value, wherein the delta V value is an RMS voltage ofthe conductor at a minimum RMS current of the conductor during the timeblock minus an RMS voltage of the conductor at a maximum RMS current ofthe conductor during the time block, wherein the delta I value is themaximum RMS current of the conductor during the time block minus theminimum RMS current of the conductor during the time block; furthercomprising a multi-channel recorder configured for generating a supplyimpedance value by subtracting the dynamic resistance value of a firstof the power quality nodes, coupled to a supply conductor, from thedynamic resistance value of a second of the power quality nodes, coupledto a load conductor; and wherein the multi-channel recorder isconfigured for generating a report based on changes in the supplyimpedance value over many time blocks.
 23. The system of claim 22,wherein each power quality node is configured for synchronizing theelectrical value sets generated by that power quality node with theelectrical value sets generated by the other power quality nodes; andwherein the multi-channel recorder is configured for synchronizing thepower quality nodes.